alloc.cc File Reference
Include dependency graph for alloc.cc:

Namespaces

 roboptim
 defined(EIGEN_RUNTIME_NO_MALLOC) && !defined(ROBOPTIM_DO_NOT_CHECK_ALLOCATION)
 

Functions

ROBOPTIM_DLLAPI bool roboptim::is_malloc_allowed_update (bool update=false, bool new_value=false)
 Update the static variable used for Eigen::set_is_malloc_allowed. More...
 
roboptim::Selection::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: selection.hxx:65
roboptim::GenericTwiceDifferentiableFunction::GenericTwiceDifferentiableFunction
GenericTwiceDifferentiableFunction(size_type inputSize, size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: twice-differentiable-function.hxx:29
roboptim::detail::ROBOPTIM_CORE_DECLARE_AUTOPROMOTE
ROBOPTIM_CORE_DECLARE_AUTOPROMOTE(GenericNumericQuadraticFunction< EigenMatrixDense >, GenericQuadraticFunction< EigenMatrixDense >)
roboptim::DerivableParametrizedFunction::jacobian
jacobian_t jacobian(const_argument_ref argument, size_type order=0) const
Computes the jacobian.
Definition: derivable-parametrized-function.hh:107
roboptim::GenericConstantFunction::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: constant.hh:71
roboptim::visualization::Gnuplot::make_interactive_gnuplot
static Gnuplot make_interactive_gnuplot(bool with_header=true)
Instanciate a Gnuplot suitable for interactive use.
Definition: gnuplot.hh:68
roboptim::visualization::matplotlib::discreteInterval_t
Function::discreteInterval_t discreteInterval_t
Import discrete interval type from function.
Definition: matplotlib-function.hh:38
roboptim::visualization::Gnuplot::operator<<
Gnuplot & operator<<(gnuplot::Command)
Definition: gnuplot.cc:59
roboptim::NTimesDerivableFunction< 2 >::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: n-times-derivable-function.hh:158
roboptim::FunctionPool::listOutputSize
static size_type listOutputSize(const functionList_t &functions)
Get the output size from the function list.
Definition: function-pool.hxx:278
roboptim::ParametrizedFunction::result_t
F result_t
Import result type.
Definition: parametrized-function.hh:65
finite-difference-gradient.hh
roboptim::Bind::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: bind.hxx:102
roboptim::Minus::right
V & right()
Definition: minus.hh:62
roboptim::detail::StateParameterPrintVisitor::operator()
void operator()(const T &val) const
Definition: solver-state.hxx:61
roboptim::visualization::gnuplot::plot_jac
Command plot_jac(const GenericDifferentiableFunction< T > &f, typename GenericDifferentiableFunction< T >::const_argument_ref arg)
Plot the Jacobian structure with Gnuplot.
Definition: gnuplot-differentiable-function.hh:69
roboptim::GenericConstantFunction::GenericConstantFunction
GenericConstantFunction(size_type input_size, const_vector_ref offset)
Build a constant function.
Definition: constant.hh:56
roboptim::fg::green
std::ostream & green(std::ostream &o)
Definition: terminal-color.hh:63
roboptim::DummySolver::DummySolver
DummySolver(const problem_t &problem)
Build a solver from a problem.
Definition: dummy.cc:28
roboptim::visualization::gnuplot::detail::sparse_matrix_to_gnuplot
std::string sparse_matrix_to_gnuplot(GenericFunctionTraits< EigenMatrixSparse >::const_matrix_ref mat)
Definition: gnuplot-matrix.cc:87
roboptim::visualization::Matplotlib::operator<<
Matplotlib & operator<<(matplotlib::Command)
Definition: matplotlib.cc:142
roboptim::Result::~Result
virtual ~Result()
Definition: result.cc:45
roboptim::GenericSolver::getMinimum
const T & getMinimum()
Get real result.
Definition: generic-solver.hh:134
parametrized-function.hh
roboptim::detail::StructuredInputJacobianInternal< FuncType, roboptim::EigenMatrixDense >::JacBlock
differentiableFunction_t::jacobian_ref JacBlock
return type of the getJacobianBlock() method
Definition: structured-input.hh:60
roboptim::GenericNumericLinearFunction::b
vector_ref b()
Definition: numeric-linear-function.hh:75
ROBOPTIM_DEBUG_ONLY
#define ROBOPTIM_DEBUG_ONLY(X)
Definition: include/roboptim/core/debug.hh:45
roboptim::SolverWarning::print
virtual std::ostream & print(std::ostream &) const
Display the problem on the specified output stream.
Definition: solver-warning.cc:30
roboptim::checkJacobian
bool checkJacobian(const GenericDifferentiableFunction< T > &function, typename GenericDifferentiableFunction< T >::const_argument_ref x, typename GenericDifferentiableFunction< T >::value_type threshold=finiteDifferenceThreshold)
Check if a Jacobian is valid.
Definition: finite-difference-gradient.hxx:300
numeric-linear-function.hh
roboptim::visualization::matplotlib::figure
ROBOPTIM_DLLAPI Command figure()
roboptim::GenericFiniteDifferenceGradient::xEps_
argument_t xEps_
Definition: decorator/finite-difference-gradient.hh:335
roboptim::LRUCache::cend
const_iterator cend() const
Iterator to the end of the cache.
Definition: cache.hxx:161
roboptim::GenericConstantFunction::~GenericConstantFunction
~GenericConstantFunction()
Definition: constant.hh:64
roboptim::GenericConstantFunction::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref) const
Definition: constant.hh:89
roboptim::GenericFunction::names_t
std::vector< name_t > names_t
Type of a vector of function argument names.
Definition: function.hh:216
roboptim::operator+
boost::shared_ptr< Plus< U, V > > operator+(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: plus.hh:95
roboptim::detail::ProductDifferentiation::Types::rowVectorU_ref
U::rowVector_ref rowVectorU_ref
Definition: product.hxx:53
minus.hxx
derivative-size.hh
roboptim::Product::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: product.hxx:272
roboptim::normalize
double normalize(double x)
Apply normalize to a scalar.
Definition: util.hxx:141
roboptim::GenericFiniteDifferenceGradient::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::GenericFunction::intervals_t
std::vector< interval_t > intervals_t
Vector of intervals.
Definition: function.hh:243
roboptim::finiteDifferenceGradientPolicies::Simple::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
function-pool.hxx
io.hh
roboptim::GenericFunction::getLowerBound
static value_type getLowerBound(const discreteInterval_t &interval)
Get the lower bound of a discrete interval.
Definition: function.hh:337
roboptim::CachedFunction::traits_t
T::traits_t traits_t
Import traits type.
Definition: cached-function.hh:63
roboptim::DerivableParametrizedFunction::impl_gradient
virtual void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0, size_type order=0) const =0
Gradient evaluation.
roboptim::LRUCache::map_t
boost::unordered_map< mapKey_t, typename valuePool_t::iterator > map_t
Map from map's key to iterator in the value pool.
Definition: cache.hh:75
roboptim::Derivative::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< typename U::traits_t >)
roboptim::detail::ProductDifferentiation::Types::vectorV_t
V::vector_t vectorV_t
Definition: product.hxx:47
roboptim::is_malloc_allowed_update
ROBOPTIM_DLLAPI bool is_malloc_allowed_update(bool update=false, bool new_value=false)
Update the static variable used for Eigen::set_is_malloc_allowed.
Definition: alloc.cc:22
roboptim::incendl
ROBOPTIM_DLLAPI std::ostream & incendl(std::ostream &o)
Increment the indentation, print an end of line, and set the indentation.
Definition: indent.cc:64
roboptim::detail::EvaluateConstraintViolation::size_type
P::size_type size_type
Definition: optimization-logger.hxx:73
roboptim::GenericTwiceDifferentiableFunction::setZero
void setZero(hessian_ref symmetric) const
Set a symmetric matrix to zero.
Definition: twice-differentiable-function.hh:176
roboptim::detail::EvaluateConstraintViolation::uniformNorm
value_type uniformNorm() const
Definition: optimization-logger.hxx:94
roboptim::fg::yellow
std::ostream & yellow(std::ostream &o)
Definition: terminal-color.hh:70
roboptim::Parameter::description
std::string description
Parameter description (for humans).
Definition: solver.hh:63
roboptim::OptimizationLogger::callback_t
solver_t::callback_t callback_t
Definition: optimization-logger.hh:50
roboptim::visualization::gnuplot::clear
ROBOPTIM_DLLAPI Command clear()
Make a Gnuplot clear command.
roboptim::Problem::constraints
const constraints_t & constraints() const
Retrieve constraints.
Definition: problem.hxx:316
roboptim::finiteDifferenceGradientPolicies::Policy::adaptee_
const GenericFunction< T > & adaptee_
Wrapped function.
Definition: decorator/finite-difference-gradient.hh:182
roboptim::DerivableParametrizedFunction::size_type
F::size_type size_type
Import size type.
Definition: derivable-parametrized-function.hh:44
gnuplot-function.hh
roboptim::detail::ProductDifferentiation::Types::gradientU_t
U::gradient_t gradientU_t
Definition: product.hxx:56
roboptim::GenericDifferentiableFunction::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: differentiable-function.hxx:80
roboptim::callback::Multiplexer::attach
void attach()
Register the multiplexer with the solver.
Definition: multiplexer.hxx:98
roboptim::FunctionPool::function_t
detail::shared_ptr_variant< functionTypeList_t >::type function_t
Type of the functions in the pool.
Definition: function-pool.hh:72
roboptim::Polynomial::~Polynomial
virtual ~Polynomial()
Definition: polynomial.hh:50
roboptim::detail::StructuredInputJacobianInternal< FuncType, roboptim::EigenMatrixDense >::differentiableFunction_t
roboptim::GenericDifferentiableFunction< typename FuncType::traits_t > differentiableFunction_t
Differentiable function type.
Definition: structured-input.hh:57
roboptim::CachedFunction::interval_t
GenericDifferentiableFunction< traits_t >::interval_t interval_t
Import interval type.
Definition: cached-function.hh:70
roboptim::Sin::impl_hessian
void impl_hessian(hessian_ref hessian, const_argument_ref x, size_type) const
Hessian evaluation.
Definition: sin.hh:105
polynomial.hh
roboptim::DerivableParametrizedFunction::isValidJacobian
bool isValidJacobian(const_jacobian_ref jacobian) const
Check if the jacobian is valid (check sizes).
Definition: derivable-parametrized-function.hh:96
solver-error.hh
roboptim::Selection::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: selection.hxx:75
roboptim::detail::AutopromoteTrait< Plus< U, V > >::T_type
Plus< U, V >::parent_t T_type
Definition: autopromote.hh:88
roboptim::BadGradient::print
virtual std::ostream & print(std::ostream &o) const
Display the exception on the specified output stream.
Definition: finite-difference-gradient.hxx:95
roboptim::Cos::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: cos.hh:52
roboptim::GenericFunction::inputSize
GenericFunction< T >::size_type inputSize() const
Return the input size (i.e.
Definition: function.hh:436
roboptim::callback::Multiplexer::solverState_t
SolverState< problem_t > solverState_t
Type of the state of the solver.
Definition: multiplexer.hh:51
roboptim::BadJacobian::maxDeltaCol_
size_type maxDeltaCol_
Definition: decorator/finite-difference-gradient.hh:122
roboptim::StateParameter::value_type
F::value_type value_type
Definition: solver-state.hh:46
roboptim::GenericSolver::~GenericSolver
virtual ~GenericSolver()
Definition: generic-solver.cc:41
roboptim::SparseFunction
GenericFunction< EigenMatrixSparse > SparseFunction
Sparse function.
Definition: fwd.hh:69
roboptim::Scalar::origin
boost::shared_ptr< U > & origin()
Definition: scalar.hh:55
roboptim::GenericFunction::getLowerBound
static value_type getLowerBound(const interval_t &interval)
Get the lower bound of an interval.
Definition: function.hh:281
roboptim::ParametrizedFunction::const_argument_ref
F::const_argument_ref const_argument_ref
Definition: parametrized-function.hh:68
roboptim::StorageOrder
static const int StorageOrder
Default matrix storage order.
Definition: function.hh:125
roboptim::SelectionById::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::GenericTwiceDifferentiableFunction::hessian
void hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
Compute the hessian at a given point.
Definition: twice-differentiable-function.hh:121
roboptim::LRUCache::mapKey_t
hash_t mapKey_t
Key type for the underlying map.
Definition: cache.hh:70
quadratic-function.hh
roboptim::EigenMatrixSparse
Tag type for functions using Eigen sparse matrices.
Definition: fwd.hh:61
roboptim::detail::jacobian_from_gradients
void jacobian_from_gradients(DifferentiableFunction::matrix_ref jac, const std::vector< const T * > &c, DifferentiableFunction::const_vector_ref x)
Definition: util.hxx:28
roboptim::GenericTwiceDifferentiableFunction::isValidHessian
bool isValidHessian(const_hessian_ref hessian) const
Check if the hessian is valid (check sizes).
Definition: twice-differentiable-function.hh:93
roboptim::detail::derives_from_differentiable_function
Checks whether the function type derives from DifferentiableFunction or DifferentiableSparseFunction.
Definition: utility.hh:242
roboptim::detail::StateParameterPrint
void StateParameterPrint(std::ostream &o, const T &val)
Print the value of a state parameter.
Definition: solver-state.hxx:32
roboptim::GenericSolver::GenericSolver
GenericSolver()
Definition: generic-solver.cc:29
roboptim::detail::AutopromoteTrait
Definition: autopromote.hh:73
cache.hxx
roboptim::GenericNumericLinearFunction::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericLinearFunction< T >)
roboptim::CachedFunction::jacobianCache_
jacobianCache_t jacobianCache_
Definition: cached-function.hh:171
roboptim::fg::warn
std::ostream & warn(std::ostream &o)
Definition: terminal-color.hh:104
roboptim::Derivative::parentType_t
DerivativeParent< U >::result_t parentType_t
Definition: derivative.hh:60
sys.hh
destroy
ROBOPTIM_DLLEXPORT void destroy(solver_t *p)
Definition: dummy-td.cc:85
roboptim::DummySolverTd::~DummySolverTd
virtual ~DummySolverTd()
Definition: dummy-td.cc:48
roboptim::visualization::Gnuplot
Gnuplot script.
Definition: gnuplot.hh:49
roboptim::detail::EvaluateConstraint::const_argument_ref
P::function_t::const_argument_ref const_argument_ref
Definition: optimization-logger.hxx:42
roboptim::visualization::gnuplot::show
ROBOPTIM_DLLAPI Command show(const char *var)
Make a Gnuplot show command.
Definition: gnuplot-commands.cc:118
roboptim::detail::ProductDifferentiation::Types
Some useful types for product differentiation.
Definition: product.hxx:36
matplotlib-commands.hh
roboptim::LRUCache::value_t
V value_t
Type of values.
Definition: cache.hh:51
roboptim::Problem::vector_t
function_t::vector_t vector_t
Vector type.
Definition: problem.hh:298
roboptim::Result::constraints
vector_t constraints
Constraints final values.
Definition: result.hh:71
roboptim::detail::ProductDifferentiation::Types::vectorV_ref
V::vector_ref vectorV_ref
Definition: product.hxx:49
roboptim::visualization::gnuplot::discreteInterval_t
Function::discreteInterval_t discreteInterval_t
Import discrete interval type from function.
Definition: gnuplot-function.hh:37
roboptim::finiteDifferenceGradientPolicies::FivePointsRule::computeJacobian
void computeJacobian(value_type epsilon, jacobian_ref jacobian, const_argument_ref argument, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:709
roboptim::BadJacobian::x_
argument_t x_
Jacobian has been computed for this point.
Definition: decorator/finite-difference-gradient.hh:109
roboptim::visualization::gnuplot::replot
ROBOPTIM_DLLAPI Command replot()
Make a Gnuplot replot command.
roboptim::Cos::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: cos.hh:58
roboptim::GenericConstantFunction::GenericConstantFunction
GenericConstantFunction(const_vector_ref offset)
Build a constant function.
Definition: constant.hh:44
roboptim::DerivableFunction
DifferentiableFunction DerivableFunction
Legacy name of TwiceDifferentiableFunction.
Definition: derivable-function.hh:26
roboptim::SolverState::print
virtual std::ostream & print(std::ostream &) const
Display the solver state on the specified output stream.
Definition: solver-state.hxx:175
roboptim::FunctionPool::impl_jacobian
virtual void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: function-pool.hxx:227
roboptim::OptimizationLogger::problem_t
solver_t::problem_t problem_t
Definition: optimization-logger.hh:45
roboptim::BadGradient::BadGradient
BadGradient(const_argument_ref x, const_gradient_ref analyticalGradient, const_gradient_ref finiteDifferenceGradient, const value_type &threshold)
Default constructor.
Definition: finite-difference-gradient.hxx:60
roboptim::GenericDifferentiableFunction::jacobianSize_t
std::pair< size_type, size_type > jacobianSize_t
Jacobian size type (pair of values).
Definition: differentiable-function.hh:90
roboptim::Map::Map
Map(boost::shared_ptr< U > origin, size_type repeat)
Map operator constructor.
Definition: map.hxx:26
roboptim::ResultWithWarnings::ResultWithWarnings
ResultWithWarnings(const Function::size_type inputSize, const Function::size_type outputSize=1)
Instantiate the class from an input/output size.
Definition: result-with-warnings.cc:31
roboptim::Bind::Bind
Bind(boost::shared_ptr< U > origin, const boundValues_t &selector)
Definition: bind.hxx:27
roboptim::Split::impl_derivative
virtual void impl_derivative(gradient_ref derivative, value_type argument, size_type order=1) const
Definition: split.hxx:146
roboptim::fg::blue
std::ostream & blue(std::ostream &o)
Definition: terminal-color.hh:77
roboptim::GenericDummySolverLastState::problem_t
parent_t::problem_t problem_t
Problem type.
Definition: dummy-laststate.hh:49
roboptim::Result::inputSize
size_type inputSize
Input size (i.e. argument size).
Definition: result.hh:63
roboptim::DifferentiableSparseFunction
GenericDifferentiableFunction< EigenMatrixSparse > DifferentiableSparseFunction
Sparse differentiable function.
Definition: fwd.hh:77
roboptim::StateParameter::~StateParameter
virtual ~StateParameter()
Virtual destructor.
Definition: solver-state.hh:64
roboptim::GenericNumericQuadraticFunction::~GenericNumericQuadraticFunction
~GenericNumericQuadraticFunction()
Definition: numeric-quadratic-function.hxx:56
roboptim::DerivableParametrizedFunction::jacobian_t
F::jacobian_t jacobian_t
Import jacobian type.
Definition: derivable-parametrized-function.hh:59
roboptim::CachedFunction::CachedFunction
CachedFunction(boost::shared_ptr< T > fct, size_t size=10)
Cache a RobOptim function.
Definition: cached-function.hxx:82
roboptim::operator*
boost::shared_ptr< Product< U, V > > operator*(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: product.hh:100
roboptim::GenericFunction::GenericFunction
GenericFunction(size_type inputSize, size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: function.hh:550
map.hh
roboptim::detail::ProductDifferentiation::Types::gradient_t
Product< U, V >::gradient_t gradient_t
Definition: product.hxx:58
roboptim::Scalar
Multiply by a constant scalar value.
Definition: fwd.hh:86
roboptim::detail::is_compatible_list
Check that CLIST_ is a subset of CLIST (i.e.
Definition: utility.hh:286
roboptim::selection
boost::shared_ptr< Selection< U > > selection(boost::shared_ptr< U > origin, typename Selection< U >::size_type start=0, typename Selection< U >::size_type size=1)
Definition: selection.hh:86
roboptim::Problem< F, boost::mpl::vector<> >::function_t
F function_t
Function type.
Definition: problem.hh:81
roboptim::detail::PrintSolverParameter::PrintSolverParameter
PrintSolverParameter(std::ostream &o)
Definition: solver.cc:28
roboptim::GenericDummySolverLastState::GenericDummySolverLastState
GenericDummySolverLastState(const problem_t &problem)
Build a solver from a problem.
Definition: dummy-laststate.hxx:24
roboptim::GenericNumericLinearFunction::impl_compute
void impl_compute(result_ref, const_argument_ref) const
Definition: numeric-linear-function.hxx:67
roboptim::detail::StructuredInput::getNumBlocks
size_t getNumBlocks() const
Returns the number of blocks defined by the function.
Definition: structured-input.hxx:51
roboptim::NTimesDerivableFunction< 2 >::derivative
derivative_t derivative(value_type argument, size_type order=1) const
Compute the derivative of the function.
Definition: n-times-derivable-function.hh:129
roboptim::OptimizationLogger::OptimizationLogger
OptimizationLogger(solver_t &solver, const boost::filesystem::path &path, bool selfRegister=true)
Constructor.
Definition: optimization-logger.hxx:195
roboptim::NTimesDerivableFunction< 2 >::derivabilityOrderMax
virtual size_type derivabilityOrderMax() const
Returns the maximum derivability order (relevant for N>2 only)
Definition: n-times-derivable-function.hh:70
roboptim::visualization::Matplotlib
matplotlib script
Definition: matplotlib.hh:50
roboptim::detail::StructuredInput::getInputBlock
ConstSegment getInputBlock(typename FuncType::const_argument_ref input, size_t blockInd) const
Reads a specified block of data from an input argument.
Definition: structured-input.hxx:57
roboptim::OptimizationLogger
Log the optimization process (values, Jacobians, time taken etc.).
Definition: optimization-logger.hh:41
roboptim::Solver::parameters_t
std::map< std::string, Parameter > parameters_t
Map of parameters.
Definition: solver.hh:97
roboptim::GenericFunction::~GenericFunction
virtual ~GenericFunction()
Trivial destructor.
Definition: function.hh:562
problem.hh
roboptim::NTimesDerivableFunction::derivabilityOrderMax
virtual size_type derivabilityOrderMax() const
Returns the maximum derivability order.
Definition: n-times-derivable-function.hh:292
roboptim::visualization::matplotlib::show
ROBOPTIM_DLLAPI Command show()
roboptim::GenericNumericLinearFunction::A
const_matrix_ref A() const
Definition: numeric-linear-function.hh:60
roboptim::Concatenate::Concatenate
Concatenate(boost::shared_ptr< U > left, boost::shared_ptr< U > right)
Definition: concatenate.hxx:47
chain.hxx
roboptim::Problem::Problem
friend class Problem
Definition: problem.hh:273
roboptim::visualization::gnuplot::reset
ROBOPTIM_DLLAPI Command reset()
Make a Gnuplot reset command.
roboptim::DerivableParametrizedFunction::gradient
void gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0, size_type order=0) const
Computes the gradient.
Definition: derivable-parametrized-function.hh:157
selection-by-id.hh
roboptim::Minus::~Minus
~Minus()
Definition: minus.hxx:49
roboptim::visualization::Gnuplot::clear
void clear()
Clear the vector of commands.
Definition: gnuplot.cc:72
roboptim::LRUCache::insert
void insert(const_key_ref key, const_value_ref value)
Insert a value into the cache.
Definition: cache.hxx:71
roboptim::CachedFunction::hessianCache_
std::vector< hessianCache_t > hessianCache_
Definition: cached-function.hh:172
roboptim::Derivative::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref x, size_type functionId=0) const
Definition: derivative.hh:113
roboptim::LRUCache::hasher_t
H hasher_t
Hasher type.
Definition: cache.hh:57
roboptim::GenericSolver::result_
result_t result_
Optimization result.
Definition: generic-solver.hh:141
roboptim::DerivableParametrizedFunction::matrix_t
F::matrix_t matrix_t
Import matrix type.
Definition: derivable-parametrized-function.hh:48
roboptim::DerivableParametrizedFunction::gradient_t
F::gradient_t gradient_t
Import gradient type.
Definition: derivable-parametrized-function.hh:55
numeric-quadratic-function.hh
roboptim::copySparseBlock
void copySparseBlock(U &matrix, const U &block, Function::size_type startRow, Function::size_type startCol, bool compress=false)
Copy a sparse block into a sparse matrix.
Definition: util.hxx:111
roboptim::CachedFunction::impl_jacobian
virtual void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: cached-function.hxx:251
solver-state.hxx
roboptim::visualization::matplotlib::comment
ROBOPTIM_DLLAPI Command comment(const char *)
Make a matplotlib comment.
Definition: matplotlib-commands.cc:100
roboptim::DerivableParametrizedFunction::jacobian_ref
F::jacobian_ref jacobian_ref
Definition: derivable-parametrized-function.hh:60
roboptim::Selection::origin
boost::shared_ptr< U > & origin()
Definition: selection.hh:58
roboptim::Plus::right
const boost::shared_ptr< V > & right() const
Definition: plus.hh:57
roboptim::DerivableSparseFunction
DifferentiableSparseFunction DerivableSparseFunction
Legacy name of TwiceDifferentiableSparseFunction.
Definition: derivable-function.hh:29
roboptim::decindent
ROBOPTIM_DLLAPI std::ostream & decindent(std::ostream &o)
Decrement the indentation.
Definition: indent.cc:41
roboptim::finiteDifferenceGradientPolicies::Policy::~Policy
virtual ~Policy()
Virtual destructor.
Definition: decorator/finite-difference-gradient.hh:158
roboptim::GenericTwiceDifferentiableFunction::impl_hessian
virtual void impl_hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const =0
Hessian evaluation.
roboptim::Bind::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::GenericIdentityFunction::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref, size_type idFunction) const
Definition: identity.hh:100
roboptim::bind
boost::shared_ptr< Bind< U > > bind(boost::shared_ptr< U > origin, const typename Bind< U >::boundValues_t &boundValues)
Definition: bind.hh:85
roboptim::indent
ROBOPTIM_DLLAPI long int & indent(std::ostream &o)
The current indentation level for o.
Definition: indent.cc:28
destroy
ROBOPTIM_DLLEXPORT void destroy(solver_t *p)
Definition: dummy-d-sparse-laststate.cc:53
roboptim::GenericFunction
Define an abstract mathematical function ( ).
Definition: function.hh:160
generic-solver.hh
roboptim::Problem::ROBOPTIM_CORE_DEPRECATED
scalingVect_t scalesVect_t ROBOPTIM_CORE_DEPRECATED
Vector of scaling vectors (deprecated typedef).
Definition: problem.hh:344
n-times-derivable-function.hh
roboptim::detail::contains_base_of
Whether a sequence of types contains a base of a given type.
Definition: utility.hh:171
roboptim::detail::impl_print
std::ostream & impl_print(std::ostream &o, const T &t)
Definition: problem.hxx:549
roboptim::TwiceDifferentiableFunction
GenericTwiceDifferentiableFunction< EigenMatrixDense > TwiceDifferentiableFunction
Definition: fwd.hh:123
roboptim::GenericSolver::result_t
boost::variant< NoSolution, Result, ResultWithWarnings, SolverError > result_t
Result type.
Definition: generic-solver.hh:73
roboptim::LRUCache::begin
iterator begin()
Iterator to the beginning of the cache.
Definition: cache.hxx:140
roboptim::detail::ConcatenateTypes::isNotDense_t
boost::disable_if< boost::is_same< traits_t, EigenMatrixDense > > isNotDense_t
Definition: concatenate.hxx:41
roboptim::GenericDummySolverLastState::solve
virtual void solve()
Implement the solve algorithm.
Definition: dummy-laststate.hxx:52
roboptim::detail::ConvertConstraintVariant::operator()
P::constraint_t operator()(const U &c) const
Definition: utility.hh:335
map.hxx
roboptim::detail::StateParameterPrintVisitor
Visitor used to print state parameters (variant).
Definition: solver-state.hxx:52
roboptim::GenericFunction::impl_compute
virtual void impl_compute(result_ref result, const_argument_ref argument) const =0
Function evaluation.
roboptim::Chain::right
V & right()
Definition: chain.hh:78
roboptim::Concatenate
Concatenate the output of two functions.
Definition: concatenate.hh:45
roboptim::callback::Multiplexer::Multiplexer
Multiplexer(solver_t &solver)
Default constructor containing no callback.
Definition: multiplexer.hxx:29
getTypeIdOfConstraintsList
const ROBOPTIM_DLLEXPORT char * getTypeIdOfConstraintsList()
Definition: dummy.cc:75
roboptim::OptimizationLogger::callbackCallId
unsigned callbackCallId() const
Return the callback iteration index.
Definition: optimization-logger.hxx:581
sin.hh
roboptim::visualization::matplotlib::detail::dense_matrix_to_matplotlib
std::string dense_matrix_to_matplotlib(GenericFunctionTraits< EigenMatrixDense >::const_matrix_ref mat, MatrixPlotType::Type type)
Definition: matplotlib-matrix.cc:62
roboptim::detail::ProductDifferentiation::Types::jacobianV_ref
V::jacobian_ref jacobianV_ref
Definition: product.hxx:67
roboptim::detail::promote2< T1, T2, 0 >::T_promote
T2 T_promote
Definition: autopromote.hh:163
roboptim::Plus
Sum two RobOptim functions.
Definition: fwd.hh:82
roboptim::SolverState::constraintViolation_
boost::optional< value_type > constraintViolation_
Current constraint violation.
Definition: solver-state.hh:163
roboptim::Map::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: map.hxx:69
roboptim::detail::derives_from_ntimes_derivable_function
Checks whether the function type derives from NTimesDerivableFunction.
Definition: utility.hh:262
fwd.hh
roboptim::CachedFunction::cache_
std::vector< functionCache_t > cache_
Definition: cached-function.hh:169
roboptim::visualization::matplotlib::Command
matplotlib command.
Definition: matplotlib-commands.hh:78
roboptim::Selection
Select a block of a function's output.
Definition: selection.hh:37
roboptim::finiteDifferenceGradientPolicies::Simple::Simple
Simple(const GenericFunction< T > &adaptee)
Definition: decorator/finite-difference-gradient.hh:203
roboptim::DerivableParametrizedFunction::const_gradient_ref
F::gradient_ref const_gradient_ref
Definition: derivable-parametrized-function.hh:57
roboptim::Cos::Cos
Cos()
Build a cosinus function.
Definition: cos.hh:40
roboptim::detail::const_eigen_ref
Return the type of a const reference to an Eigen matrix, using Eigen's Ref.
Definition: utility.hh:106
roboptim::GenericSolver::reset
void reset()
Force to restart the optimization.
Definition: generic-solver.cc:46
roboptim::finiteDifferenceGradientPolicies::Simple
Fast finite difference gradient computation.
Definition: decorator/finite-difference-gradient.hh:195
roboptim::SolverError::SolverError
SolverError(const std::string &arg)
Instantiate an error from an error message.
Definition: solver-error.cc:25
roboptim::detail::ProductDifferentiation::Types::vectorU_t
U::vector_t vectorU_t
Definition: product.hxx:46
roboptim::EigenMatrixDense
Tag type for functions using Eigen dense matrices.
Definition: fwd.hh:59
roboptim::BadJacobian::maxDelta_
value_type maxDelta_
Maximum error.
Definition: decorator/finite-difference-gradient.hh:118
roboptim::detail::derives_from_function
Checks whether the function types derives from Function or SparseFunction.
Definition: utility.hh:232
roboptim::Result
Represents the solution of an optimization problem.
Definition: result.hh:39
ROBOPTIM_CORE_VERSION
#define ROBOPTIM_CORE_VERSION
Definition: config.hh:20
roboptim::detail::PrecisionTrait
Definition: autopromote.hh:28
roboptim::detail::cast_constraint_type::type
boost::mpl::fold< CLIST, void, boost::mpl::if_< boost::is_base_of< boost::mpl::_2, C >, boost::mpl::if_< boost::is_void< boost::mpl::_1 >, boost::mpl::_2, detail::get_descendant< boost::mpl::_1, boost::mpl::_2 > >, boost::mpl::_1 > >::type type
Definition: utility.hh:223
roboptim::ParametrizedFunction::size_type
F::size_type size_type
Import size type.
Definition: parametrized-function.hh:59
roboptim::Problem< F, boost::mpl::vector<> >::argument_t
function_t::argument_t argument_t
Argument type.
Definition: problem.hh:90
roboptim::detail::StructuredInput::ConstSegment
Eigen::Ref< const typename FuncType::argument_t >::ConstSegmentReturnType ConstSegment
return type of the getInputBlock() method
Definition: structured-input.hh:132
config.hh
roboptim::derivative
boost::shared_ptr< Derivative< U > > derivative(boost::shared_ptr< U > origin, typename Derivative< U >::size_type variableId=0)
Definition: derivative.hh:132
roboptim::visualization::gnuplot::Command
Gnuplot command.
Definition: gnuplot-commands.hh:41
roboptim::Product::right
const boost::shared_ptr< V > & right() const
Definition: product.hh:57
roboptim::CachedFunction::functionCache_t
LRUCache< cacheKey_t, vector_t, Hasher > functionCache_t
Definition: cached-function.hh:75
roboptim::detail::ProductDifferentiation
Utility structure used for product differentiation.
Definition: product.hxx:30
roboptim::Map::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: map.hxx:55
roboptim::finiteDifferenceGradientPolicies::FivePointsRule::computeGradient
void computeGradient(value_type epsilon, gradient_ref gradient, const_argument_ref argument, size_type idFunction, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:612
roboptim::FunctionPool
A pool of functions that will be processed together.
Definition: function-pool.hh:57
roboptim::GenericFunction::makeUpperInterval
static interval_t makeUpperInterval(value_type u)
Construct an interval from an upper bound.
Definition: function.hh:273
getSizeOfProblem
ROBOPTIM_DLLEXPORT std::size_t getSizeOfProblem()
Definition: dummy-d-sparse-laststate.cc:37
roboptim::detail::CachedFunctionTypes::cachedFunction_t
CachedFunction< T > cachedFunction_t
Definition: cached-function.hxx:50
roboptim::detail::add_shared_ptr
Transform a types list into a types list of shared pointers.
Definition: utility.hh:56
roboptim::LRUCache::keyTracker_t
std::list< hash_t > keyTracker_t
List used to track key usage.
Definition: cache.hh:67
differentiable-function.hh
matplotlib.hh
linear-function.hh
roboptim::GenericFunction::traits_t
T traits_t
Traits type.
Definition: function.hh:167
roboptim::BadGradient
Exception thrown when a gradient check fails.
Definition: decorator/finite-difference-gradient.hh:38
roboptim::callback::Multiplexer::callback_t
solver_t::callback_t callback_t
Callback function type.
Definition: multiplexer.hh:54
roboptim::DerivableParametrizedFunction::jacobianSize_t
F::jacobianSize_t jacobianSize_t
Import jacobian size type (pair of values).
Definition: derivable-parametrized-function.hh:64
roboptim::visualization::gnuplot::cd
ROBOPTIM_DLLAPI Command cd(const char *dir)
Make a Gnuplot cd command.
roboptim::GenericFunction::makeInfiniteInterval
static interval_t makeInfiniteInterval()
Construct an infinite interval.
Definition: function.hh:257
roboptim::Map::MapShPtr_t
boost::shared_ptr< Map > MapShPtr_t
Definition: map.hh:48
roboptim::DifferentiableFunction
GenericDifferentiableFunction< EigenMatrixDense > DifferentiableFunction
Dense differentiable function.
Definition: fwd.hh:73
roboptim::LRUCache::end
iterator end()
Iterator to the end of the cache.
Definition: cache.hxx:147
roboptim::SolverWarning::SolverWarning
SolverWarning(const std::string &arg)
Instantiate the class with a message.
Definition: solver-warning.cc:24
roboptim::LRUCache::print
virtual std::ostream & print(std::ostream &) const
Display the cache on the specified output stream.
Definition: cache.hxx:215
roboptim::NTimesDerivableFunction
Define a function, derivable n times ( ).
Definition: fwd.hh:142
alloc.hh
roboptim::LRUCache::find
const_iterator find(const_key_ref key) const
Find an element in the cache.
Definition: cache.hxx:168
roboptim::Split::Split
Split(boost::shared_ptr< const T > fct, size_type functionId)
Split operator constructor.
Definition: split.hxx:42
roboptim::visualization::matplotlib::detail::set_red_yellow_blue_cmap
std::string set_red_yellow_blue_cmap()
Definition: matplotlib-matrix.cc:48
roboptim::Split::traits_t
T::traits_t traits_t
Import traits type.
Definition: split.hh:37
roboptim::visualization::matplotlib::MatrixPlotType::Structure
@ Structure
Definition: matplotlib-matrix.hh:44
roboptim::Concatenate::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: concatenate.hxx:134
roboptim::DummySolverTd::parent_t
Solver< TwiceDifferentiableFunction, boost::mpl::vector< TwiceDifferentiableFunction > > parent_t
Define parent's type.
Definition: dummy-td.hh:40
roboptim::GenericDifferentiableFunction::impl_gradient
virtual void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const =0
Gradient evaluation.
roboptim::Scalar::origin
const boost::shared_ptr< U > & origin() const
Definition: scalar.hh:50
roboptim::checkJacobianAndThrow
void checkJacobianAndThrow(const GenericDifferentiableFunction< T > &function, typename GenericDifferentiableFunction< T >::const_argument_ref x, typename GenericDifferentiableFunction< T >::value_type threshold=finiteDifferenceThreshold)
Definition: finite-difference-gradient.hxx:316
gnuplot-matrix.hh
roboptim::detail::ConcatenateTypes::isDense_t
boost::enable_if< boost::is_same< traits_t, EigenMatrixDense > > isDense_t
Definition: concatenate.hxx:37
roboptim::Problem::function_t
F function_t
Function type.
Definition: problem.hh:285
solver-factory.hh
roboptim::Sin::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref x, size_type) const
Definition: sin.hh:82
roboptim::map
boost::shared_ptr< Map< U > > map(boost::shared_ptr< U > origin, typename U::size_type repeat)
Definition: map.hh:88
roboptim::Scalar::Scalar
Scalar(boost::shared_ptr< U > fct, value_type scalar)
Create a scalar operator.
Definition: scalar.hxx:26
roboptim::decendl
ROBOPTIM_DLLAPI std::ostream & decendl(std::ostream &o)
Decrement the indentation, print an end of line, and set the indentation.
Definition: indent.cc:69
util.hh
roboptim::GenericDummySolverLastState::parent_t
Solver< F, boost::mpl::vector< F > > parent_t
Define parent's type.
Definition: dummy-laststate.hh:46
roboptim::Result::print
virtual std::ostream & print(std::ostream &o) const
Display the result on the specified output stream.
Definition: result.cc:50
roboptim::Problem::scalingVector
const scalingVect_t & scalingVector() const
Retrieve constraints scaling vector.
Definition: problem.hxx:477
roboptim::DerivableParametrizedFunction::impl_jacobian
virtual void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg, size_type order=0) const
Jacobian evaluation.
Definition: derivable-parametrized-function.hh:200
roboptim::GenericSumOfC1Squares::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::fg::reset
std::ostream & reset(std::ostream &o)
Definition: terminal-color.hh:49
roboptim::detail::AutopromoteTrait::T_type
T T_type
Definition: autopromote.hh:75
quadratic-function.hxx
roboptim::GenericFunction::outputSize
GenericFunction< T >::size_type outputSize() const
Return the output size (i.e.
Definition: function.hh:444
roboptim::GenericFunction::epsilon
static value_type epsilon()
Get the value of the machine epsilon, useful for floating types comparison.
Definition: function.hh:222
terminal-color.hh
roboptim::DerivableParametrizedFunction::isValidGradient
bool isValidGradient(const_gradient_ref gradient) const
Check if the gradient is valid (check size).
Definition: derivable-parametrized-function.hh:87
roboptim::visualization::matplotlib::Command::isPlot
const bool & isPlot() const
Definition: matplotlib-commands.cc:65
getTypeIdOfConstraintsList
const ROBOPTIM_DLLEXPORT char * getTypeIdOfConstraintsList()
Definition: dummy-laststate.cc:42
roboptim::Bind::BindShPtr_t
boost::shared_ptr< Bind > BindShPtr_t
Definition: bind.hh:48
roboptim::OptimizationLogger::differentiableFunction_t
GenericDifferentiableFunction< traits_t > differentiableFunction_t
Definition: optimization-logger.hh:57
roboptim::GenericNumericQuadraticFunction::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericQuadraticFunction< T >)
roboptim::Problem::startingPoint_t
boost::optional< argument_t > startingPoint_t
Optional vector defines a starting point.
Definition: problem.hh:310
roboptim::DerivableParametrizedFunction::jacobian
void jacobian(jacobian_ref jacobian, const_argument_ref argument, size_type order=0) const
Computes the jacobian.
Definition: derivable-parametrized-function.hh:123
roboptim::NTimesDerivableFunction< 2 >::derivativeSize
size_type derivativeSize() const
Return the size of the derivative vector.
Definition: n-times-derivable-function.hh:80
roboptim::Chain::~Chain
~Chain()
Definition: chain.hxx:55
roboptim::SelectionById::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: selection-by-id.hxx:80
roboptim::detail::ROBOPTIM_CORE_DECLARE_PRECISION
ROBOPTIM_CORE_DECLARE_PRECISION(GenericFunction< EigenMatrixDense >, 1)
roboptim::IdentityFunction
GenericIdentityFunction< EigenMatrixDense > IdentityFunction
Definition: fwd.hh:107
roboptim::detail::check_constraint_type
Checks whether C is a valid constraint type in CLIST.
Definition: utility.hh:199
bind.hxx
ROBOPTIM_DLLAPI
#define ROBOPTIM_DLLAPI
Definition: portability.hh:63
roboptim::Chain::left
const boost::shared_ptr< U > & left() const
Definition: chain.hh:63
roboptim::NTimesDerivableFunction< 2 >::impl_hessian
void impl_hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
Hessian evaluation.
Definition: n-times-derivable-function.hh:254
roboptim::operator-
boost::shared_ptr< Minus< U, V > > operator-(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: minus.hh:95
roboptim::GenericDifferentiableFunction::isValidGradient
bool isValidGradient(const_gradient_ref gradient) const
Check if the gradient is valid (check size).
Definition: differentiable-function.hh:112
roboptim::GenericFunctionTraits< EigenMatrixSparse >::size_type
matrix_t::Index size_type
Definition: function.hh:672
roboptim::BadJacobian::~BadJacobian
virtual ~BadJacobian()
Definition: finite-difference-gradient.hxx:202
solver.hh
roboptim::LRUCache::valuePool_t
std::vector< value_t > valuePool_t
Definition: cache.hh:62
roboptim::Parameter
Solver parameter type.
Definition: solver.hh:45
roboptim::finiteDifferenceGradientPolicies::Simple::policy_t
Policy< T > policy_t
Definition: decorator/finite-difference-gradient.hh:201
roboptim::Polynomial
Polynomial function.
Definition: polynomial.hh:37
roboptim::NTimesDerivableFunction< 2 >::~NTimesDerivableFunction
virtual ~NTimesDerivableFunction()
Definition: n-times-derivable-function.hh:75
roboptim::GenericLinearFunction::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericQuadraticFunction< T >)
roboptim::Scalar::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: scalar.hxx:45
roboptim::GenericQuadraticFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: quadratic-function.hxx:32
roboptim::Solver::callback_t
boost::function< void(const problem_t &problem, solverState_t &state)> callback_t
Per-iteration callback type.
Definition: solver.hh:111
roboptim::detail::CachedFunctionTypes
Definition: cached-function.hh:44
roboptim::GenericFiniteDifferenceGradient::impl_compute
virtual void impl_compute(result_ref, const_argument_ref) const
Function evaluation.
Definition: finite-difference-gradient.hxx:236
dummy-laststate.hh
roboptim::GenericDummySolverLastState::~GenericDummySolverLastState
virtual ~GenericDummySolverLastState()
Definition: dummy-laststate.hxx:47
roboptim::Hasher::operator()
std::size_t operator()(roboptim::Function::const_argument_ref x) const
Definition: cached-function.hh:35
roboptim::GenericFiniteDifferenceGradient::epsilon_
const value_type epsilon_
Definition: decorator/finite-difference-gradient.hh:333
roboptim::GenericFunctionTraits
GenericFunction traits.
Definition: function.hh:139
roboptim::operator<<
std::ostream & operator<<(std::ostream &o, const LRUCache< K, V, H > &cache)
Definition: cache.hxx:232
roboptim::SelectionById::SelectionById
SelectionById(boost::shared_ptr< U > origin, std::vector< bool > selector)
Definition: selection-by-id.hxx:26
roboptim::Problem::intervalsVect_t
std::vector< intervals_t > intervalsVect_t
Vector of interval vectors.
Definition: problem.hh:333
roboptim::detail::list_converter
Converts CLIST to a boost::mpl::vector to ensure a similar behavior for codes using different random ...
Definition: utility.hh:161
roboptim::DummySolverTd
Dummy solver which always fails.
Definition: dummy-td.hh:34
roboptim::DerivableParametrizedFunction
Parametrized function with parameter derivative available.
Definition: derivable-parametrized-function.hh:38
roboptim::Selection::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: selection.hxx:56
roboptim::Solver
Solver for a specific problem class.
Definition: fwd.hh:140
roboptim::Concatenate::ConcatenateShPtr_t
boost::shared_ptr< Concatenate > ConcatenateShPtr_t
Definition: concatenate.hh:54
getSizeOfProblem
ROBOPTIM_DLLEXPORT std::size_t getSizeOfProblem()
Definition: dummy-td.cc:70
roboptim::GenericFunction::isValidResult
bool isValidResult(const_result_ref result) const
Check the given result size is valid.
Definition: function.hh:428
roboptim::SelectionById::~SelectionById
~SelectionById()
Definition: selection-by-id.hxx:56
roboptim::SolverState< problem_t >
roboptim::GenericTwiceDifferentiableFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: twice-differentiable-function.hxx:38
roboptim::Solver::print
virtual std::ostream & print(std::ostream &) const
Display the solver on the specified output stream.
Definition: solver.hxx:93
roboptim::BadGradient::~BadGradient
virtual ~BadGradient()
Definition: finite-difference-gradient.hxx:90
roboptim::SelectionById::origin
const boost::shared_ptr< U > & origin() const
Definition: selection-by-id.hh:48
roboptim::detail::ConcatenateTypes
Definition: concatenate.hh:36
roboptim::GenericConstantFunction
Constant function.
Definition: constant.hh:35
roboptim::LRUCache::const_key_ref
detail::const_ref< key_t >::type const_key_ref
Type of const reference to key.
Definition: cache.hh:48
roboptim::detail::BlockProvider
Gives access to a std::vector of std::pair<size_t, size_t> representing blocks of input Those blocks ...
Definition: structured-input.hh:36
roboptim::GenericSolver::logger
static log4cxx::LoggerPtr logger
Pointer to function logger (see log4cxx documentation).
Definition: generic-solver.hh:144
roboptim::Product::ProductShPtr_t
boost::shared_ptr< Product > ProductShPtr_t
Definition: product.hh:42
roboptim::LRUCache
LRU (Least Recently Used) cache.
Definition: cache.hh:41
roboptim::SelectionById::SelectionByIdShPtr_t
boost::shared_ptr< SelectionById > SelectionByIdShPtr_t
Definition: selection-by-id.hh:42
roboptim::Problem::argumentBounds
intervals_t & argumentBounds()
Retrieve arguments bounds.
Definition: problem.hxx:463
roboptim::Concatenate::~Concatenate
~Concatenate()
Definition: concatenate.hxx:75
roboptim::visualization::matplotlib::set
ROBOPTIM_DLLAPI Command set(const char *var, const char *val)
Make a matplotlib set command.
Definition: matplotlib-commands.cc:116
roboptim::Result::Result
Result(const size_type inputSize, const size_type outputSize=1)
Instantiate a result and fix input/output sizes.
Definition: result.cc:30
roboptim::detail::LogJacobianConstraint
Definition: optimization-logger.hxx:123
solver_t
DummySolverTd::parent_t solver_t
Definition: dummy-td.cc:63
roboptim::GenericNumericQuadraticFunction::b
vector_ref b()
Definition: numeric-quadratic-function.hh:97
roboptim::detail::StateParameterPrintVisitor::o_
std::ostream & o_
Definition: solver-state.hxx:66
roboptim::GenericLinearFunction
Define an abstract linear function.
Definition: fwd.hh:130
roboptim::detail::CachedFunctionTypes::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericTwiceDifferentiableFunction< traits_t >)
roboptim::Map::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::detail::getBlock
Eigen::Block< Derived > getBlock(Eigen::SparseMatrixBase< Derived > &m, int colStart, int colLength)
Definition: structured-input.hxx:31
derivable-function.hh
roboptim::OptimizationLogger::~OptimizationLogger
virtual ~OptimizationLogger()
Destructor.
Definition: optimization-logger.hxx:228
numeric-quadratic-function.hxx
roboptim::visualization::matplotlib::Command::command
const std::string & command() const
Retrieve the command as a string.
Definition: matplotlib-commands.cc:71
roboptim::DerivableParametrizedFunction::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: derivable-parametrized-function.hh:172
roboptim::CachedFunction::jacobianCache_t
LRUCache< cacheKey_t, jacobian_t, Hasher > jacobianCache_t
Definition: cached-function.hh:77
roboptim::Problem::constraint_t
detail::shared_ptr_variant< constraintsList_t >::type constraint_t
Constraint's type.
Definition: problem.hh:292
roboptim::Chain::Chain
Chain(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Chain operator constructor.
Definition: chain.hxx:26
roboptim::GenericNumericLinearFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: numeric-linear-function.hxx:108
roboptim::GenericFiniteDifferenceGradient::~GenericFiniteDifferenceGradient
~GenericFiniteDifferenceGradient()
Definition: finite-difference-gradient.hxx:229
product.hh
roboptim::visualization::matplotlib::title
ROBOPTIM_DLLAPI Command title(const char *argument)
roboptim::OptimizationLogger::path
const boost::filesystem::path & path() const
Return the path of the log directory.
Definition: optimization-logger.hxx:554
roboptim::callback::Multiplexer::perIterationCallbackUnsafe
virtual void perIterationCallbackUnsafe(const problem_t &pb, solverState_t &state)
Meta-callback calling multiple callbacks.
Definition: multiplexer.hxx:85
roboptim::SolverState::function_t
P::function_t function_t
Import function type from problem.
Definition: solver-state.hh:91
roboptim::BadJacobian::threshold_
value_type threshold_
Allowed threshold.
Definition: decorator/finite-difference-gradient.hh:125
getSizeOfProblem
ROBOPTIM_DLLEXPORT std::size_t getSizeOfProblem()
Definition: dummy-laststate.cc:37
roboptim::GenericIdentityFunction::GenericIdentityFunction
GenericIdentityFunction(const_vector_ref offset)
Build an identity function.
Definition: identity.hh:43
roboptim::ParametrizedFunction::inputSize
size_type inputSize() const
Return the input size (i.e.
Definition: parametrized-function.hxx:44
roboptim::detail::vector_to_array
ROBOPTIM_DLLAPI void vector_to_array(Function::value_type *dst, Function::const_vector_ref src)
Definition: util.cc:30
roboptim::Product::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: product.hxx:239
roboptim::Product::left
U & left()
Definition: product.hh:52
roboptim::FunctionPool::impl_gradient
virtual void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: function-pool.hxx:216
n-times-derivable-function.hxx
roboptim::DummySolver::~DummySolver
virtual ~DummySolver()
Definition: dummy.cc:48
roboptim::sparse_to_dense
ROBOPTIM_DLLAPI GenericFunctionTraits< EigenMatrixDense >::matrix_t sparse_to_dense(GenericFunctionTraits< EigenMatrixSparse >::const_matrix_ref m)
Convert a sparse matrix into a dense matrix.
Definition: util.cc:61
roboptim::Problem::names_t
function_t::names_t names_t
Vector of names (e.g. for arguments).
Definition: problem.hh:325
roboptim::detail::LogJacobianConstraint::differentiableFunction_t
GenericDifferentiableFunction< traits_t > differentiableFunction_t
Type of differentiable functions.
Definition: optimization-logger.hxx:132
roboptim::Minus::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::GenericSumOfC1Squares::baseFunction
const boost::shared_ptr< const parent_t > & baseFunction() const
Get base function Base function is the vector valued function given at construction of this class.
Definition: sum-of-c1-squares.hxx:56
roboptim::Solver< F, boost::mpl::vector< F > >::problem_t
Problem< F, boost::mpl::vector< F > > problem_t
Solver problem type.
Definition: solver.hh:91
minus.hh
multiplexer.hh
roboptim::detail::ProductDifferentiation::Types::gradientV_ref
V::gradient_ref gradientV_ref
Definition: product.hxx:60
roboptim::GenericLinearFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: linear-function.hxx:43
roboptim::fg::ok
std::ostream & ok(std::ostream &o)
Definition: terminal-color.hh:92
roboptim::GenericFunction::makeDiscreteInterval
static discreteInterval_t makeDiscreteInterval(interval_t interval, value_type step)
Construct a discrete interval.
Definition: function.hh:325
roboptim::CachedFunction::hessianCache_t
LRUCache< cacheKey_t, hessian_t, Hasher > hessianCache_t
Definition: cached-function.hh:78
roboptim::Product::parentType_t
detail::PromoteTrait< U, V >::T_promote parentType_t
Definition: product.hh:39
roboptim::Parameter::parameterValues_t
boost::variant< value_type, vector_t, int, std::string, bool > parameterValues_t
Allowed types for solver parameters:
Definition: solver.hh:57
roboptim::DummySolver::parent_t
Solver< Function, boost::mpl::vector< Function > > parent_t
Define parent's type.
Definition: dummy.hh:38
roboptim::product
boost::shared_ptr< Product< U, V > > product(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: product.hh:93
roboptim::SolverState::parameters_t
std::map< std::string, StateParameter< function_t > > parameters_t
Map of parameters.
Definition: solver-state.hh:94
roboptim::GenericLinearFunction::GenericLinearFunction
GenericLinearFunction(size_type inputSize, size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: linear-function.hxx:26
matplotlib-function.hh
roboptim::Solver::setIterationCallback
virtual void setIterationCallback(callback_t)
Set the per-iteration callback.
Definition: solver.hh:169
roboptim::detail::row_vector_stride::type
Eigen::InnerStride<(SO==Eigen::RowMajor)? 1:-1 > type
Definition: utility.hh:146
roboptim::TwiceDifferentiableSparseFunction
GenericTwiceDifferentiableFunction< EigenMatrixSparse > TwiceDifferentiableSparseFunction
Definition: fwd.hh:127
roboptim::GenericLinearFunction::impl_hessian
void impl_hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
Definition: linear-function.hxx:34
roboptim::fg::orange
std::ostream & orange(std::ostream &o)
Definition: terminal-color.hh:84
roboptim::GenericFunction::size_type
GenericFunctionTraits< T >::size_type size_type
Size type.
Definition: function.hh:204
roboptim::Plus::PlusShPtr_t
boost::shared_ptr< Plus > PlusShPtr_t
Definition: plus.hh:42
roboptim::visualization::gnuplot::plot
Command plot(const GenericFunction< T > &f, discreteInterval_t interval)
Plot a 1D function with Gnuplot.
Definition: gnuplot-function.hh:62
roboptim::LRUCache::const_mapKey_ref
detail::const_ref< mapKey_t >::type const_mapKey_ref
Definition: cache.hh:71
roboptim::finiteDifferenceGradientPolicies::Policy::Policy
Policy(const GenericFunction< T > &adaptee)
Definition: decorator/finite-difference-gradient.hh:151
roboptim::DummySolverLastState
GenericDummySolverLastState< Function > DummySolverLastState
Definition: dummy-laststate.hh:86
roboptim::visualization::gnuplot::detail::set_matrix_header
void set_matrix_header(std::string &str, typename GenericFunctionTraits< T >::const_matrix_ref mat)
Definition: gnuplot-matrix.cc:36
roboptim::Problem::scalingVect_t
std::vector< scaling_t > scalingVect_t
Vector of scaling vectors.
Definition: problem.hh:341
roboptim::Selection::origin
const boost::shared_ptr< U > & origin() const
Definition: selection.hh:53
roboptim::detail::PromoteTrait::T1
AutopromoteTrait< T1_orig >::T_type T1
Definition: autopromote.hh:170
roboptim::SumOfC1Squares
GenericSumOfC1Squares< EigenMatrixDense > SumOfC1Squares
Sum of the squares of dense differentiable functions.
Definition: sum-of-c1-squares.hh:90
roboptim::detail::ConvertConstraintVariant
Convert a constraint from a Boost.Variant to an adequate constraint type depending on the problem's c...
Definition: utility.hh:331
roboptim::ParametrizedFunction::vector_t
F::vector_t vector_t
Import vector type.
Definition: parametrized-function.hh:61
gnuplot-differentiable-function.hh
roboptim::Product::Product
Product(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: product.hxx:203
roboptim::Product::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: product.hxx:249
roboptim::OptimizationLogger::perIterationCallbackUnsafe
virtual void perIterationCallbackUnsafe(const typename solver_t::problem_t &pb, const typename solver_t::solverState_t &state)
Definition: optimization-logger.hxx:482
roboptim::Split::interval_t
DifferentiableFunction::interval_t interval_t
Import interval type.
Definition: split.hh:43
roboptim::detail::StateParameterPrint< bool >
void StateParameterPrint< bool >(std::ostream &o, const bool &val)
Definition: solver-state.hxx:38
roboptim::Sin::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: sin.hh:52
roboptim::Cos::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref x) const
Definition: cos.hh:99
roboptim::FunctionPool::callback_ptr
boost::shared_ptr< callback_t > callback_ptr
Definition: function-pool.hh:79
roboptim::GenericNumericQuadraticFunction::symmetric_t
matrix_t symmetric_t
Symmetric matrix type.
Definition: numeric-quadratic-function.hh:45
roboptim::detail::LogJacobianConstraint::operator()
boost::enable_if< boost::is_base_of< differentiableFunction_t, U > >::type operator()(const boost::shared_ptr< U > &constraint) const
Definition: optimization-logger.hxx:154
roboptim::detail::ConstraintName
Definition: optimization-logger.hxx:59
roboptim::iendl
ROBOPTIM_DLLAPI std::ostream & iendl(std::ostream &o)
Print an end of line, then set the indentation.
Definition: indent.cc:54
roboptim::GenericNumericQuadraticFunction
Build a quadratic function from a matrix and a vector.
Definition: fwd.hh:98
roboptim::GenericDummySolverLastState::callback_
callback_t callback_
Intermediate callback (called at each end of iteration).
Definition: dummy-laststate.hh:80
roboptim::SolverState::parameters
const parameters_t & parameters() const
Definition: solver-state.hxx:139
roboptim::GenericNumericQuadraticFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: numeric-quadratic-function.hxx:142
roboptim::GenericFunction::getUpperBound
static value_type getUpperBound(const interval_t &interval)
Get the upper bound of an interval.
Definition: function.hh:289
roboptim::Selection::Selection
Selection(boost::shared_ptr< U > fct, size_type start, size_type size)
Create a selection given an input function and a block.
Definition: selection.hxx:26
roboptim::GenericFunctionTraits< EigenMatrixDense >::value_type
double value_type
Value type.
Definition: function.hh:599
twice-differentiable-function.hh
roboptim::GenericNumericLinearFunction::impl_jacobian
void impl_jacobian(jacobian_ref, const_argument_ref) const
Definition: numeric-linear-function.hxx:79
roboptim::GenericSumOfC1Squares::impl_gradient
virtual void impl_gradient(gradient_ref gradient, const_argument_ref x, size_type row=0) const
Gradient.
Definition: sum-of-c1-squares.hxx:76
roboptim::detail::AutopromoteTrait< Minus< U, V > >::T_type
Minus< U, V >::parent_t T_type
Definition: autopromote.hh:94
roboptim::callback::Multiplexer::solver_t
S solver_t
Type of the solver.
Definition: multiplexer.hh:45
roboptim::detail::PromoteTrait::T2
AutopromoteTrait< T2_orig >::T_type T2
Definition: autopromote.hh:171
roboptim::visualization::matplotlib::Command::~Command
~Command()
Definition: matplotlib-commands.cc:61
roboptim::FunctionPool::functionTypeList_t
detail::list_converter< FLIST >::type functionTypeList_t
Definition: function-pool.hh:68
roboptim::SolverFactory::~SolverFactory
~SolverFactory()
Unload the plug-in and free the instantiated solver.
Definition: solver-factory.hxx:201
roboptim::LinearFunction
GenericLinearFunction< EigenMatrixDense > LinearFunction
Definition: fwd.hh:130
ROBOPTIM_GENERATE_TRAITS_REFS_T
#define ROBOPTIM_GENERATE_TRAITS_REFS_T(NAME, TRAITS)
Definition: function.hh:83
roboptim::visualization::matplotlib::Import::~Import
~Import()
Import destructor.
Definition: matplotlib-commands.cc:41
roboptim::Problem< F, boost::mpl::vector<> >::constraintsList_t
boost::mpl::vector constraintsList_t
Definition: problem.hh:75
cache.hh
roboptim::Chain::left
U & left()
Definition: chain.hh:68
roboptim::visualization::gnuplot::detail::dense_jacobian_to_gnuplot
std::string dense_jacobian_to_gnuplot(DifferentiableFunction::const_jacobian_ref jac, const std::string &name)
Definition: gnuplot-differentiable-function.cc:36
roboptim::BadJacobian::maxDeltaRow_
size_type maxDeltaRow_
Components containing the maximum error.
Definition: decorator/finite-difference-gradient.hh:121
roboptim::LRUCache::const_iterator
map_t::const_iterator const_iterator
Definition: cache.hh:77
roboptim::Derivative::origin
boost::shared_ptr< U > & origin()
Definition: derivative.hh:99
roboptim::GenericTwiceDifferentiableFunction::hessian
hessian_t hessian(const_argument_ref argument, size_type functionId=0) const
Compute the hessian at a given point.
Definition: twice-differentiable-function.hh:106
roboptim::Chain::right
const boost::shared_ptr< V > & right() const
Definition: chain.hh:73
roboptim::Cos::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref x, size_type) const
Definition: cos.hh:83
roboptim::LRUCache::clear
void clear()
Clear the cache.
Definition: cache.hxx:64
roboptim::DerivableParametrizedFunction::const_argument_ref
F::const_argument_ref const_argument_ref
Definition: derivable-parametrized-function.hh:53
roboptim::Derivative::~Derivative
~Derivative()
Definition: derivative.hh:91
roboptim::Map::~Map
~Map()
Definition: map.hxx:49
roboptim::detail::EvaluateConstraint::EvaluateConstraint
EvaluateConstraint(const_argument_ref x)
Definition: optimization-logger.hxx:44
roboptim::ResultWithWarnings::warnings
std::vector< SolverWarning > warnings
Vector of warnings.
Definition: result-with-warnings.hh:57
gnuplot.hh
roboptim::Chain::parentType_t
detail::PromoteTrait< U, V >::T_promote parentType_t
Definition: chain.hh:51
roboptim::GenericFiniteDifferenceGradient::impl_jacobian
virtual void impl_jacobian(jacobian_ref jacobian, const_argument_ref argument) const
Jacobian evaluation.
Definition: finite-difference-gradient.hxx:255
roboptim::Solver::getParameter
const T & getParameter(const std::string &key) const
Definition: solver.hxx:71
roboptim::GenericDifferentiableFunction::gradient
void gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Computes the gradient.
Definition: differentiable-function.hh:190
roboptim::Map::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: map.hxx:88
roboptim::detail::CachedFunctionTypes::isNotDifferentiable_t
boost::disable_if< detail::derives_from_differentiable_function< T > > isNotDifferentiable_t
Definition: cached-function.hxx:61
dummy.hh
roboptim::NTimesDerivableFunction< 2 >::isValidDerivative
bool isValidDerivative(const_derivative_ref derivative) const
Check if a derivative is valid (check sizes).
Definition: n-times-derivable-function.hh:88
roboptim::FunctionPool::functionList_t
std::vector< function_t > functionList_t
List of functions that will populate the pool.
Definition: function-pool.hh:75
roboptim::Problem< F, boost::mpl::vector<> >::names_t
function_t::names_t names_t
Vector of names (e.g. for arguments).
Definition: problem.hh:108
roboptim::DerivableParametrizedFunction::vector_t
F::vector_t vector_t
Import vector type.
Definition: derivable-parametrized-function.hh:46
roboptim::visualization::gnuplot::Command::Command
Command(std::string cmd)
Make a command from a string.
Definition: gnuplot-commands.cc:32
roboptim::GenericFunction::name_t
std::string name_t
Type of a function argument name.
Definition: function.hh:213
roboptim::Problem< F, boost::mpl::vector<> >::vector_t
function_t::vector_t vector_t
Vector type.
Definition: problem.hh:87
roboptim::detail::CachedFunctionTypes::isNotNTimesDerivable_t
boost::disable_if< detail::derives_from_ntimes_derivable_function< T > > isNotNTimesDerivable_t
Definition: cached-function.hxx:77
roboptim::finiteDifferenceGradientPolicies::Policy::computeJacobian
virtual void computeJacobian(value_type epsilon, jacobian_ref jacobian, const_argument_ref argument, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:444
roboptim::LRUCache::iterator
map_t::iterator iterator
Definition: cache.hh:78
roboptim::StateParameter::value
stateParameterValues_t value
Value.
Definition: solver-state.hh:70
roboptim::visualization::matplotlib::detail::set_red_white_blue_cmap
std::string set_red_white_blue_cmap()
Definition: matplotlib-matrix.cc:35
roboptim::visualization::matplotlib::plot
Command plot(const GenericFunction< T > &f, discreteInterval_t interval)
Plot a 1D function with matplotlib.
Definition: matplotlib-function.hh:52
roboptim::GenericSumOfC1Squares::GenericSumOfC1Squares
GenericSumOfC1Squares(const boost::shared_ptr< parent_t > &function, const std::string &name)
Constructor by vector valued functions The value of this scalar valued function is the sum of the squ...
Definition: sum-of-c1-squares.hxx:27
roboptim::DerivableParametrizedFunction::jacobianSize
jacobianSize_t jacobianSize() const
Return the jacobian size as a pair.
Definition: derivable-parametrized-function.hh:78
roboptim::detail::ProductDifferentiation::Types::gradientV_t
V::gradient_t gradientV_t
Definition: product.hxx:57
roboptim::detail::CachedFunctionTypes::isTwiceDifferentiable_t
boost::enable_if< detail::derives_from_twice_differentiable_function< T > > isTwiceDifferentiable_t
Definition: cached-function.hxx:65
roboptim::detail::is_compatible_list::type
boost::mpl::fold< CLIST_, boost::mpl::bool_< true >, boost::mpl::if_< contains_base_of< CLIST, boost::mpl::_2 >, boost::mpl::_1, boost::mpl::bool_< false > > >::type type
Definition: utility.hh:306
roboptim::LinearSparseFunction
GenericLinearFunction< EigenMatrixSparse > LinearSparseFunction
Definition: fwd.hh:132
roboptim::BadJacobian::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::visualization::matplotlib::MatrixPlotType::Values
@ Values
Definition: matplotlib-matrix.hh:42
getTypeIdOfConstraintsList
const ROBOPTIM_DLLEXPORT char * getTypeIdOfConstraintsList()
Definition: dummy-d-sparse-laststate.cc:42
roboptim::GenericNumericQuadraticFunction::impl_compute
void impl_compute(result_ref, const_argument_ref) const
Definition: numeric-quadratic-function.hxx:63
roboptim::plus
boost::shared_ptr< Plus< U, V > > plus(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: plus.hh:88
roboptim::Solver::~Solver
virtual ~Solver()
Definition: solver.hxx:43
roboptim::Problem< F, boost::mpl::vector<> >::ROBOPTIM_CORE_DEPRECATED
scaling_t scales_t ROBOPTIM_CORE_DEPRECATED
Scaling vector (deprecated typedef).
Definition: problem.hh:105
roboptim::detail::EvaluateConstraintViolation::computeViolation
value_type computeViolation(const value_type &x, const interval_t &x_ul) const
Definition: optimization-logger.hxx:84
create
ROBOPTIM_DLLEXPORT solver_t * create(const DummyDifferentiableSparseSolverLastState::problem_t &pb)
Definition: dummy-d-sparse-laststate.cc:48
derivative.hh
roboptim::detail::LogJacobianConstraint::jacobian_t
differentiableFunction_t::jacobian_t jacobian_t
Jacobian type.
Definition: optimization-logger.hxx:141
roboptim::Solver< TwiceDifferentiableFunction, boost::mpl::vector< TwiceDifferentiableFunction > >::parameters_
parameters_t parameters_
Solver parameters (run-time configuration).
Definition: solver.hh:186
roboptim::Plus::~Plus
~Plus()
Definition: plus.hxx:49
roboptim::GenericFunction::getStep
static value_type getStep(const discreteInterval_t &interval)
Get the upper step of a discrete interval.
Definition: function.hh:355
roboptim::GenericNumericLinearFunction::GenericNumericLinearFunction
GenericNumericLinearFunction(const_matrix_ref A, const_vector_ref b)
Build a linear function from a matrix and a vector.
Definition: numeric-linear-function.hxx:31
roboptim::detail::PrecisionTrait::precisionRank
@ precisionRank
Definition: autopromote.hh:32
roboptim::SolverState::problem_t
P problem_t
Problem type.
Definition: solver-state.hh:82
roboptim::BadGradient::x_
argument_t x_
Gradient has been computed for this point.
Definition: decorator/finite-difference-gradient.hh:59
roboptim::detail::ProductDifferentiation::Types::size_type
Product< U, V >::size_type size_type
Definition: product.hxx:43
selection.hxx
roboptim::callback::Multiplexer::perIterationCallback
void perIterationCallback(const problem_t &pb, solverState_t &state)
Meta-callback calling multiple callbacks.
Definition: multiplexer.hxx:66
roboptim::GenericDifferentiableFunction::gradient
gradient_t gradient(const_argument_ref argument, size_type functionId=0) const
Computes the gradient.
Definition: differentiable-function.hh:172
roboptim::OptimizationLogger::logPath
const boost::filesystem::path & logPath() const
Return the path of the log directory.
Definition: optimization-logger.hxx:547
roboptim::visualization::gnuplot::Command::command_
std::string command_
Store Gnuplot command.
Definition: gnuplot-commands.hh:53
roboptim::concatenate
boost::shared_ptr< Concatenate< typename detail::PromoteTrait< U, V >::T_promote > > concatenate(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: concatenate.hh:118
roboptim::GenericDifferentiableFunction::isValidJacobian
bool isValidJacobian(const_jacobian_ref jacobian) const
Check if the jacobian is valid (check sizes).
Definition: differentiable-function.hh:121
roboptim::detail::ConstraintName::operator()
std::string operator()(const U &constraint) const
Definition: optimization-logger.hxx:62
roboptim::GenericQuadraticFunction::size_type
parent_t::size_type size_type
Definition: quadratic-function.hh:39
roboptim::FunctionPool::print
virtual std::ostream & print(std::ostream &) const
Overriden print function for pools.
Definition: function-pool.hxx:253
roboptim::Plus::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::demangle
const std::string demangle(const char *name)
Definition: solver-factory.hxx:64
roboptim::GenericDifferentiableFunction::GenericDifferentiableFunction
GenericDifferentiableFunction(size_type inputSize, size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: differentiable-function.hxx:29
chain.hh
roboptim::detail::LogJacobianConstraint::problem_t
P problem_t
Type of the problem.
Definition: optimization-logger.hxx:126
roboptim::GenericNumericQuadraticFunction::c
vector_ref c()
Definition: numeric-quadratic-function.hh:102
roboptim::set_is_malloc_allowed
bool set_is_malloc_allowed(bool allow)
Manage the calls to Eigen::set_is_malloc_allowed.
Definition: alloc.hh:40
roboptim::LRUCache::const_value_ref
detail::const_ref< value_t >::type const_value_ref
Type of const reference to key.
Definition: cache.hh:54
roboptim::detail::AutopromoteTrait< Product< U, V > >::T_type
Product< U, V >::parent_t T_type
Definition: autopromote.hh:100
roboptim::LRUCache::hash_t
std::size_t hash_t
Hash type used by the Boost map.
Definition: cache.hh:60
roboptim::SolverError::~SolverError
~SolverError()
Trivial destructor.
Definition: solver-error.cc:43
roboptim::Bind::parentType_t
detail::AutopromoteTrait< U >::T_type parentType_t
Definition: bind.hh:45
scalar.hxx
roboptim::detail::const_ref::type
boost::mpl::if_< is_eigen_type< T >, const_eigen_ref< T >, boost::add_reference< typename boost::add_const< T >::type > >::type::type type
Definition: utility.hh:132
roboptim::DerivableParametrizedFunction::DerivableParametrizedFunction
DerivableParametrizedFunction(size_type inputSize, size_type functionInputSize, size_type functionOutputSize)
Concrete class constructor should call this constructor.
Definition: derivable-parametrized-function.hh:183
finite-difference-gradient.hxx
roboptim::detail::EvaluateConstraintViolation::interval_t
P::interval_t interval_t
Definition: optimization-logger.hxx:75
roboptim::GenericNumericLinearFunction::~GenericNumericLinearFunction
~GenericNumericLinearFunction()
Definition: numeric-linear-function.hxx:60
roboptim::NTimesDerivableFunction< 2 >::impl_compute
void impl_compute(result_ref result, const_argument_ref argument) const
Function evaluation.
Definition: n-times-derivable-function.hh:186
roboptim::visualization::gnuplot::pwd
ROBOPTIM_DLLAPI Command pwd()
Make a Gnuplot pwdcommand.
roboptim::detail::is_eigen_type
Check whether the type provided is an Eigen type.
Definition: utility.hh:94
roboptim::DerivableParametrizedFunction::value_type
F::value_type value_type
Import value type.
Definition: derivable-parametrized-function.hh:42
roboptim::GenericConstantFunction::impl_compute
void impl_compute(result_ref result, const_argument_ref) const
Definition: constant.hh:78
roboptim::finiteDifferenceGradientPolicies::Policy
Interface for the finite difference gradient policies.
Definition: decorator/finite-difference-gradient.hh:145
roboptim::unionCast
T * unionCast(void *ptr)
Definition: solver-factory.hxx:38
roboptim::Solver::problem_
const problem_t problem_
Problem that will be solved.
Definition: solver.hh:183
roboptim::FunctionPool::FunctionPool
FunctionPool(const callback_ptr callback, const functionList_t &functions, const std::string &name="")
FunctionPool constructor.
Definition: function-pool.hxx:174
roboptim::GenericSolver::SOLVER_NO_SOLUTION
@ SOLVER_NO_SOLUTION
Solution has yet to be found.
Definition: generic-solver.hh:54
roboptim::GenericNumericQuadraticFunction::impl_hessian
void impl_hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
Definition: numeric-quadratic-function.hxx:135
roboptim::detail::EvaluateConstraint::operator()
P::vector_t operator()(const U &constraint) const
Definition: optimization-logger.hxx:49
roboptim::Solver< F, boost::mpl::vector< F > >::solverState_t
SolverState< problem_t > solverState_t
State of the solver.
Definition: solver.hh:100
roboptim::callback::Multiplexer::callbacks
callbacks_t & callbacks()
Return the vector of callbacks.
Definition: multiplexer.hxx:52
roboptim::fg::red
std::ostream & red(std::ostream &o)
Definition: terminal-color.hh:56
roboptim::ResultWithWarnings
Represents the solution of an optimization problem when errors occurred during the solving process.
Definition: result-with-warnings.hh:42
roboptim::DerivableParametrizedFunction::argument_t
F::argument_t argument_t
Import argument types.
Definition: derivable-parametrized-function.hh:52
roboptim::GenericSolver
Abstract interface satisfied by all solvers.
Definition: generic-solver.hh:48
roboptim::OptimizationLogger::const_argument_ref
function_t::const_argument_ref const_argument_ref
Definition: optimization-logger.hh:55
roboptim::GenericSolver::print
virtual std::ostream & print(std::ostream &) const
Display the solver on the specified output stream.
Definition: generic-solver.cc:64
plus.hh
roboptim::callback::Multiplexer::callbacks_t
std::vector< callback_t > callbacks_t
Type of a vector of callbacks.
Definition: multiplexer.hh:57
roboptim::BadJacobian::analyticalJacobian_
gradient_t analyticalJacobian_
Analytical Jacobian.
Definition: decorator/finite-difference-gradient.hh:112
roboptim::BadGradient::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::visualization::Matplotlib::multiplot
std::pair< int, int > & multiplot()
Plots layout.
Definition: matplotlib.cc:52
roboptim::StateParameter::stateParameterValues_t
boost::variant< value_type, vector_t, int, std::string, bool > stateParameterValues_t
Allowed types for parameters:
Definition: solver-state.hh:56
roboptim::GenericDummySolverLastState::solverState_t
SolverState< problem_t > solverState_t
Type of the state of the solver.
Definition: dummy-laststate.hh:55
roboptim::NumericLinearFunction
GenericNumericLinearFunction< EigenMatrixDense > NumericLinearFunction
Definition: fwd.hh:93
roboptim::CachedFunction::impl_compute
virtual void impl_compute(result_ref result, const_argument_ref argument) const
Definition: cached-function.hxx:131
roboptim::CachedFunction::impl_derivative
virtual void impl_derivative(gradient_ref derivative, value_type argument, size_type order=1) const
Definition: cached-function.hxx:353
roboptim::Concatenate::parentType_t
detail::AutopromoteTrait< U >::T_type parentType_t
Definition: concatenate.hh:48
roboptim::detail::ProductDifferentiation::Types::fullDense_t
boost::mpl::and_< boost::is_same< typename U::traits_t, EigenMatrixDense >, boost::is_same< typename V::traits_t, EigenMatrixDense > > fullDense_t
Definition: product.hxx:41
roboptim::detail::ProductDifferentiation::Types::value_type
Product< U, V >::value_type value_type
Definition: product.hxx:44
roboptim::detail::ProductDifferentiation::Types::rowVectorV_t
V::rowVector_t rowVectorV_t
Definition: product.hxx:52
roboptim::BadGradient::analyticalGradient_
gradient_t analyticalGradient_
Analytical gradient.
Definition: decorator/finite-difference-gradient.hh:62
roboptim::Bind::origin
boost::shared_ptr< U > & origin()
Definition: bind.hh:60
roboptim::callback::Multiplexer::problem_t
solver_t::problem_t problem_t
Type of the problem.
Definition: multiplexer.hh:48
roboptim::visualization::matplotlib::Command::Command
Command(const std::string &cmd, bool isPlot=false)
Make a command from a string.
Definition: matplotlib-commands.cc:56
roboptim::CachedFunction::cachedFunctionGradient
void cachedFunctionGradient(gradient_ref gradient, const_argument_ref argument, size_type functionId, typename detail::CachedFunctionTypes< U >::isDifferentiable_t::type *=0) const
Definition: cached-function.hxx:158
roboptim::GenericNumericQuadraticFunction::b
const_vector_ref b() const
Definition: numeric-quadratic-function.hh:82
roboptim::incindent
ROBOPTIM_DLLAPI std::ostream & incindent(std::ostream &o)
Increment the indentation.
Definition: indent.cc:35
roboptim::visualization::Gnuplot::push_command
void push_command(gnuplot::Command cmd)
Add a new Gnuplot command to the script.
Definition: gnuplot.cc:43
roboptim::Bind::boundValues_t
std::vector< boost::optional< value_type > > boundValues_t
Definition: bind.hh:49
roboptim::ParametrizedFunction::operator()
result_t operator()(const_argument_ref argument) const
Evaluate the function at a specified point.
Definition: parametrized-function.hxx:35
roboptim::Bind::~Bind
~Bind()
Definition: bind.hxx:60
roboptim::Problem::interval_t
function_t::interval_t interval_t
Interval type (e.g. for bounds).
Definition: problem.hh:313
roboptim::visualization::Gnuplot::Gnuplot
Gnuplot(bool with_header=true)
Default constructor can not be called directly.
Definition: gnuplot.cc:31
roboptim
defined(EIGEN_RUNTIME_NO_MALLOC) && !defined(ROBOPTIM_DO_NOT_CHECK_ALLOCATION)
Definition: alloc.hh:32
roboptim::Problem< F, boost::mpl::vector<> >::value_type
function_t::value_type value_type
Definition: problem.hh:84
getSizeOfProblem
ROBOPTIM_DLLEXPORT std::size_t getSizeOfProblem()
Definition: dummy.cc:70
roboptim::NTimesDerivableFunction< 2 >::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Gradient evaluation.
Definition: n-times-derivable-function.hh:211
roboptim::GenericTwiceDifferentiableFunction::hessianSize
hessianSize_t hessianSize() const
Return the size of a hessian.
Definition: twice-differentiable-function.hh:84
roboptim::CachedFunction::cachedFunctionDerivative
void cachedFunctionDerivative(gradient_ref derivative, value_type argument, size_type order, typename detail::CachedFunctionTypes< U >::isNTimesDerivable_t::type *=0) const
Definition: cached-function.hxx:318
roboptim::detail::ProductDifferentiation::Types::gradient_ref
Product< U, V >::gradient_ref gradient_ref
Definition: product.hxx:61
roboptim::Sin::~Sin
~Sin()
Definition: sin.hh:45
roboptim::finiteDifferenceGradientPolicies::FivePointsRule::computeColumn
void computeColumn(value_type epsilon, gradient_ref column, const_argument_ref argument, size_type colIdx, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:741
roboptim::GenericSolver::minimumType
solutions minimumType()
Determine real minimum type.
Definition: generic-solver.hh:107
roboptim::GenericFunction::getName
const std::string & getName() const
Get function name.
Definition: function.hh:497
roboptim::visualization::gnuplot::detail::dense_matrix_to_gnuplot
std::string dense_matrix_to_gnuplot(GenericFunctionTraits< EigenMatrixDense >::const_matrix_ref mat)
Definition: gnuplot-matrix.cc:60
roboptim::visualization::Matplotlib::withHeader
bool & withHeader()
Whether to print the header (imports).
Definition: matplotlib.cc:42
cached-function.hxx
roboptim::detail::CachedFunctionTypes::traits_t
T::traits_t traits_t
Definition: cached-function.hxx:49
roboptim::Polynomial::impl_hessian
void impl_hessian(hessian_ref hessian, const_argument_ref x, size_type) const
Hessian evaluation.
Definition: polynomial.hxx:159
util.hxx
roboptim::detail::StructuredInput::addBlock
void addBlock(size_t size)
Adds a new block of input to the function.
Definition: structured-input.hxx:37
cos.hh
MATPLOTLIB_UNARY_COMMAND_ARG
#define MATPLOTLIB_UNARY_COMMAND_ARG(NAME, ARG)
Definition: matplotlib-commands.cc:91
roboptim::GenericFunctionTraits< EigenMatrixDense >::size_type
matrix_t::Index size_type
Definition: function.hh:628
roboptim::visualization::gnuplot::help
ROBOPTIM_DLLAPI Command help(const char *topic="")
Make a Gnuplot help command.
result.hh
roboptim::GenericQuadraticFunction::GenericQuadraticFunction
GenericQuadraticFunction(size_type inputSize, size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: quadratic-function.hxx:24
roboptim::LRUCache::size
size_t size() const
Size of the cache.
Definition: cache.hxx:40
BOOST_STATIC_ASSERT_MSG
BOOST_STATIC_ASSERT_MSG(Eigen::ROBOPTIM_STORAGE_ORDER==Eigen::ColMajor||Eigen::ROBOPTIM_STORAGE_ORDER==Eigen::RowMajor, "Wrong storage order provided by ROBOPTIM_STORAGE_ORDER.")
ROBOPTIM_STORAGE_ORDER.
roboptim::finiteDifferenceGradientPolicies::Policy::column_
vector_t column_
Vector storing temporary Jacobian column.
Definition: decorator/finite-difference-gradient.hh:185
ROBOPTIM_GENERATE_TYPEDEFS_EIGEN_REF_VEC
#define ROBOPTIM_GENERATE_TYPEDEFS_EIGEN_REF_VEC(NAME, TYPE)
Definition: function.hh:58
multiplexer.hxx
roboptim::GenericIdentityFunction::~GenericIdentityFunction
~GenericIdentityFunction()
Definition: identity.hh:49
roboptim::visualization::gnuplot::unset
ROBOPTIM_DLLAPI Command unset(const char *var)
Make a Gnuplot unset command.
Definition: gnuplot-commands.cc:106
MATPLOTLIB_UNARY_COMMAND
#define MATPLOTLIB_UNARY_COMMAND(NAME)
Definition: matplotlib-commands.cc:77
roboptim::Polynomial::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: polynomial.hxx:73
utility.hh
matplotlib-matrix.hh
roboptim::GenericNumericQuadraticFunction::impl_gradient
void impl_gradient(gradient_ref, const_argument_ref, size_type=0) const
Definition: numeric-quadratic-function.hxx:124
roboptim::Polynomial::Polynomial
Polynomial(const_vector_ref coefficients)
Build a polynomial function.
Definition: polynomial.hxx:31
roboptim::finiteDifferenceGradientPolicies::Policy::gradient_
gradient_t gradient_
Vector storing temporary Jacobian row.
Definition: decorator/finite-difference-gradient.hh:188
roboptim::finiteDifferenceThreshold
static const double finiteDifferenceThreshold
Default threshold for checkGradient.
Definition: decorator/finite-difference-gradient.hh:32
roboptim::Problem::constraintsList_t
detail::list_converter< CLIST >::type constraintsList_t
Constraints types list.
Definition: problem.hh:279
destroy
ROBOPTIM_DLLEXPORT void destroy(solver_t *p)
Definition: dummy-laststate.cc:53
roboptim::fg::isTtyStream
bool isTtyStream(const std::ostream &o)
Definition: terminal-color.hh:38
create
ROBOPTIM_DLLEXPORT solver_t * create(const DummySolver::problem_t &pb)
Definition: dummy.cc:80
roboptim::GenericFunctionTraits< EigenMatrixSparse >::value_type
double value_type
Value type.
Definition: function.hh:646
roboptim::Plus::left
U & left()
Definition: plus.hh:52
roboptim::TwiceDerivableFunction
TwiceDifferentiableFunction TwiceDerivableFunction
Legacy name of DifferentiableFunction.
Definition: twice-derivable-function.hh:26
roboptim::Problem::value_type
function_t::value_type value_type
Import function's value_type type.
Definition: problem.hh:295
roboptim::detail::LogJacobianConstraint::const_argument_ref
differentiableFunction_t::const_argument_ref const_argument_ref
Argument type.
Definition: optimization-logger.hxx:135
roboptim::Derivative::jacobianSize_t
std::pair< size_type, size_type > jacobianSize_t
Jacobian size type (pair of values).
Definition: derivative.hh:68
roboptim::visualization::gnuplot::Command::command
const std::string & command() const
Retrieve the command as a string.
Definition: gnuplot-commands.cc:40
roboptim::GenericIdentityFunction
Identity function.
Definition: identity.hh:34
roboptim::ParametrizedFunction::matrix_t
F::matrix_t matrix_t
Import matrix type.
Definition: parametrized-function.hh:63
roboptim::checkGradient
bool checkGradient(const GenericDifferentiableFunction< T > &function, typename GenericDifferentiableFunction< T >::size_type functionId, typename GenericDifferentiableFunction< T >::const_argument_ref x, typename GenericDifferentiableFunction< T >::value_type threshold=finiteDifferenceThreshold)
Check if a gradient is valid.
Definition: finite-difference-gradient.hxx:264
roboptim::LRUCache::resize
void resize(size_t size)
Change the size of the cache.
Definition: cache.hxx:46
roboptim::detail::CachedFunctionTypes::isDifferentiable_t
boost::enable_if< detail::derives_from_differentiable_function< T > > isDifferentiable_t
Definition: cached-function.hxx:57
roboptim::GenericDifferentiableFunction::ROBOPTIM_GENERATE_TRAITS_REFS_
ROBOPTIM_GENERATE_TRAITS_REFS_(gradient)
Gradient type.
roboptim::visualization::Gnuplot::make_gnuplot
static Gnuplot make_gnuplot(bool with_header=true)
Instanciate a Gnuplot without setting a term.
Definition: gnuplot.hh:57
roboptim::NTimesDerivableFunction::NTimesDerivableFunction
NTimesDerivableFunction(size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: n-times-derivable-function.hh:310
roboptim::Hasher
Hash generator for argument vector.
Definition: cached-function.hh:33
roboptim::Selection::SelectionShPtr_t
boost::shared_ptr< Selection > SelectionShPtr_t
Definition: selection.hh:43
roboptim::SolverState::~SolverState
virtual ~SolverState()
Definition: solver-state.hxx:91
roboptim::SolverFactory::SolverFactory
SolverFactory(std::string solver, const problem_t &problem)
Instantiate a factory and load the plug-in.
Definition: solver-factory.hxx:72
roboptim::Problem::argumentScales
scales_t & argumentScales() ROBOPTIM_CORE_DEPRECATED
Retrieve arguments scaling (deprecated version).
Definition: problem.hxx:505
dummy-laststate.hxx
GNUPLOT_UNARY_COMMAND
#define GNUPLOT_UNARY_COMMAND(NAME)
Definition: gnuplot-commands.cc:46
roboptim::Map::parentType_t
detail::AutopromoteTrait< U >::T_type parentType_t
Definition: map.hh:45
roboptim::GenericDummySolverLastState
Dummy solver which always fails, but returns the last state of the solver.
Definition: dummy-laststate.hh:41
roboptim::visualization::gnuplot::comment
ROBOPTIM_DLLAPI Command comment(const char *)
Make a Gnuplot comment.
Definition: gnuplot-commands.cc:68
roboptim::Problem
Definition: fwd.hh:139
roboptim::ParametrizedFunction::functionOutputSize
size_type functionOutputSize() const
Return the function's output size (i.e.
Definition: parametrized-function.hxx:59
roboptim::detail::PromoteTrait::promoteToT1
@ promoteToT1
Definition: autopromote.hh:191
roboptim::Cos::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericTwiceDifferentiableFunction< T >)
twice-differentiable-function.hxx
roboptim::GenericSumOfC1Squares::impl_compute
virtual void impl_compute(result_ref result, const_argument_ref x) const
Compute value of function Value is sum of squares of coordinates of vector valued base function.
Definition: sum-of-c1-squares.hxx:63
roboptim::OptimizationLogger::callback
callback_t callback()
Return the callback function.
Definition: optimization-logger.hxx:339
roboptim::Result::vector_t
Function::vector_t vector_t
Import vector type from Function class.
Definition: result.hh:45
roboptim::BadGradient::maxDelta_
value_type maxDelta_
Maximum error.
Definition: decorator/finite-difference-gradient.hh:68
roboptim::SelectionById::origin
boost::shared_ptr< U > & origin()
Definition: selection-by-id.hh:53
roboptim::SolverError::print
virtual std::ostream & print(std::ostream &) const
Display the error on the specified output stream.
Definition: solver-error.cc:48
roboptim::Problem< F, boost::mpl::vector<> >::size_type
function_t::size_type size_type
Size type.
Definition: problem.hh:93
roboptim::detail::const_ref
Return the proper const reference type of a given type.
Definition: utility.hh:126
roboptim::finiteDifferenceGradientPolicies::FivePointsRule
Precise finite difference gradient computation.
Definition: decorator/finite-difference-gradient.hh:239
roboptim::detail::ProductDifferentiation::jacobian
static void jacobian(typename Types< U, V >::jacobian_ref jac_uv, const typename Types< U, V >::vectorU_ref u, const typename Types< U, V >::vectorV_ref v, const typename Types< U, V >::jacobianU_ref jac_u, const typename Types< U, V >::jacobianV_ref jac_v, typename boost::enable_if< typename Types< U, V >::fullDense_t >::type *=0)
Full dense version of Jacobian computation.
Definition: product.hxx:124
roboptim::GenericFiniteDifferenceGradient::impl_gradient
virtual void impl_gradient(gradient_ref, const_argument_ref argument, size_type=0) const
Gradient evaluation.
Definition: finite-difference-gradient.hxx:244
roboptim::FunctionPool::listInputSize
static size_type listInputSize(const functionList_t &functions)
Get the input size from the function list.
Definition: function-pool.hxx:271
roboptim::ParametrizedFunction::ParametrizedFunction
ParametrizedFunction(size_type inputSize, size_type functionInputSize, size_type functionOutputSize)
Concrete class constructor should call this constructor.
Definition: parametrized-function.hxx:24
roboptim::BadJacobian::print
virtual std::ostream & print(std::ostream &o) const
Display the exception on the specified output stream.
Definition: finite-difference-gradient.hxx:207
solver_t
DummyDifferentiableSparseSolverLastState::parent_t solver_t
Definition: dummy-d-sparse-laststate.cc:29
roboptim::GenericConstantFunction::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref, size_type=0) const
Definition: constant.hh:83
roboptim::visualization::gnuplot::plot_xy
Command plot_xy(const GenericFunction< T > &f, discreteInterval_t interval)
Plot a 2D function with Gnuplot.
Definition: gnuplot-function.hh:122
concatenate.hh
roboptim::Problem::startingPoint
startingPoint_t & startingPoint()
Set the initial guess.
Definition: problem.hxx:436
roboptim::DummySolver::solve
virtual void solve()
Implement the solve algorithm.
Definition: dummy.cc:53
roboptim::SolverError
Base exception class for solving errors.
Definition: solver-error.hh:37
roboptim::finiteDifferenceGradientPolicies::FivePointsRule::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::detail::LogJacobianConstraint::size_type
problem_t::size_type size_type
Size type.
Definition: optimization-logger.hxx:138
roboptim::FunctionPool::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(pool_t)
roboptim::Bind
Bind some function input to a constant value.
Definition: bind.hh:42
roboptim::Solver::parameters
const parameters_t & parameters() const
Definition: solver.hxx:56
roboptim::GenericFunction::ROBOPTIM_GENERATE_TRAITS_REFS_
ROBOPTIM_GENERATE_TRAITS_REFS_(vector)
Basic (column) vector type.
roboptim::Scalar::parentType_t
detail::AutopromoteTrait< U >::T_type parentType_t
Definition: scalar.hh:38
roboptim::visualization::Matplotlib::make_matplotlib
static Matplotlib make_matplotlib(std::pair< int, int > multiplot=std::make_pair(1, 1), bool with_header=true)
Instanciate a matplotlib without setting a term.
Definition: matplotlib.hh:58
roboptim::Concatenate::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::SolverState::x
const argument_t & x() const
Retrieve the current optimization parameters.
Definition: solver-state.hxx:97
roboptim::Solver::pluginName
const std::string & pluginName() const
Definition: solver.hxx:79
selection-by-id.hxx
roboptim::detail::BlockProvider::blocks
std::vector< std::pair< size_t, size_t > > blocks
stores the blocks defined by the function as a pair of integers.
Definition: structured-input.hh:41
roboptim::SolverFactory::solver_t
T solver_t
Solver type.
Definition: solver-factory.hh:60
roboptim::Derivative::origin
const boost::shared_ptr< U > & origin() const
Definition: derivative.hh:94
roboptim::Plus::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: plus.hxx:66
roboptim::Concatenate::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: concatenate.hxx:81
roboptim::FunctionPool::callback_t
F callback_t
Type of the callback function (where the computation happens).
Definition: function-pool.hh:78
roboptim::detail::ProductDifferentiation::Types::jacobianU_ref
U::jacobian_ref jacobianU_ref
Definition: product.hxx:66
roboptim::GenericFunction::discreteInterval_t
boost::tuple< value_type, value_type, value_type > discreteInterval_t
Types representing a discrete interval.
Definition: function.hh:306
roboptim::GenericNumericQuadraticFunction::A
matrix_ref A()
Definition: numeric-quadratic-function.hh:92
roboptim::Split::impl_compute
virtual void impl_compute(result_ref result, const_argument_ref argument) const
Definition: split.hxx:59
roboptim::Plus::right
V & right()
Definition: plus.hh:62
roboptim::Concatenate::traits_t
parentType_t::traits_t traits_t
Traits type.
Definition: concatenate.hh:52
roboptim::StateParameter::description
std::string description
Parameter description (for humans).
Definition: solver-state.hh:67
create
ROBOPTIM_DLLEXPORT solver_t * create(const DummySolverTd::problem_t &pb)
Definition: dummy-td.cc:80
roboptim::detail::shared_ptr_variant
Generate a Boost.Variant of shared pointers from the static constraints types list.
Definition: utility.hh:79
roboptim::GenericDifferentiableFunction::jacobianSize
jacobianSize_t jacobianSize() const
Return the jacobian size as a pair.
Definition: differentiable-function.hh:104
roboptim::visualization::matplotlib::MatrixPlotType::Log
@ Log
Definition: matplotlib-matrix.hh:43
roboptim::BadJacobian
Exception thrown when a Jacobian check fails.
Definition: decorator/finite-difference-gradient.hh:88
roboptim::SolverState::x_
argument_t x_
Current optimization parameters.
Definition: solver-state.hh:153
roboptim::Problem::argumentNames
names_t & argumentNames()
Retrieve arguments names.
Definition: problem.hxx:519
roboptim::detail::StructuredInputJacobianInternal< FuncType, roboptim::EigenMatrixSparse >::JacBlock
Eigen::Block< typename differentiableFunction_t::jacobian_t, Eigen::Dynamic, Eigen::Dynamic, false > JacBlock
return type of the getJacobianBlock() method
Definition: structured-input.hh:95
roboptim::visualization::Matplotlib::~Matplotlib
~Matplotlib()
Definition: matplotlib.cc:38
roboptim::visualization::Matplotlib::push_import
void push_import(const matplotlib::Import &import)
Add a new Python import to the script.
Definition: matplotlib.cc:78
roboptim::detail::PromoteTrait::T_promote
promote2< T1, T2, promoteToT1 >::T_promote T_promote
Definition: autopromote.hh:194
roboptim::detail::printConstraint
Definition: problem.hxx:558
roboptim::Scalar::ScalarShPtr_t
boost::shared_ptr< Scalar > ScalarShPtr_t
Definition: scalar.hh:41
roboptim::Result::lambda
vector_t lambda
Lagrange multipliers.
Definition: result.hh:73
roboptim::finiteDifferenceGradientPolicies::FivePointsRule::compute_deriv
void compute_deriv(typename GenericFunction< T >::size_type j, double h, double &result, double &round, double &trunc, typename GenericFunction< T >::const_argument_ref argument, typename GenericFunction< T >::size_type idFunction, typename GenericFunction< T >::argument_ref xEps) const
Algorithm from the Gnu Scientific Library.
Definition: finite-difference-gradient.hxx:337
roboptim::GenericTwiceDifferentiableFunction::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::GenericFunction::makeDiscreteInterval
static discreteInterval_t makeDiscreteInterval(value_type min, value_type max, value_type step)
Construct a discrete interval.
Definition: function.hh:314
roboptim::Derivative::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: derivative.hh:106
roboptim::derivativeSize
Definition: fwd.hh:145
roboptim::NumericQuadraticFunction
GenericNumericQuadraticFunction< EigenMatrixDense > NumericQuadraticFunction
Definition: fwd.hh:98
roboptim::Problem::boundsVector
const intervalsVect_t & boundsVector() const
Retrieve constraints bounds vector.
Definition: problem.hxx:456
roboptim::Scalar::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::DummySolverTd::solve
virtual void solve()
Implement the solve algorithm.
Definition: dummy-td.cc:53
product.hxx
roboptim::GenericIdentityFunction::print
virtual std::ostream & print(std::ostream &o) const
Display the function on the specified output stream.
Definition: identity.hh:56
ROBOPTIM_CORE_FILTER_TTY
#define ROBOPTIM_CORE_FILTER_TTY()
Definition: terminal-color.hh:29
roboptim::StateParameter::vector_t
F::vector_t vector_t
Definition: solver-state.hh:47
solver_t
DummySolver::parent_t solver_t
Definition: dummy.cc:63
roboptim::GenericFunction::infinity
static value_type infinity()
Get the value that symbolizes positive infinity.
Definition: function.hh:229
parametrized-function.hxx
roboptim::detail::EvaluateConstraintViolation::EvaluateConstraintViolation
EvaluateConstraintViolation(const std::vector< vector_t > &constraints, const intervalsVect_t &bounds)
Definition: optimization-logger.hxx:78
roboptim::SolverState::cost
const boost::optional< value_type > & cost() const
Retrieve the current cost.
Definition: solver-state.hxx:111
roboptim::detail::ConvertConstraint::operator()
boost::shared_ptr< typename cast_constraint_type< C, CLIST >::type > operator()(const boost::shared_ptr< C > &c) const
Definition: utility.hh:317
roboptim::visualization::Gnuplot::print
std::ostream & print(std::ostream &) const
Display the Gnuplot script on the specified output stream.
Definition: gnuplot.cc:49
roboptim::Split::~Split
~Split()
Definition: split.hxx:53
MATPLOTLIB_UNARY_COMMAND_VAR
#define MATPLOTLIB_UNARY_COMMAND_VAR(VAR, NAME)
Definition: matplotlib-commands.cc:84
roboptim::OptimizationLogger::vector_t
solver_t::problem_t::vector_t vector_t
Definition: optimization-logger.hh:48
numeric-linear-function.hxx
roboptim::Solver::vector_t
F::vector_t vector_t
Import vector type from cost function.
Definition: solver.hh:94
roboptim::OptimizationLogger::value_type
solver_t::problem_t::value_type value_type
Definition: optimization-logger.hh:46
roboptim::NTimesDerivableFunction< 2 >::traits_t
parent_t::traits_t traits_t
Traits type.
Definition: n-times-derivable-function.hh:58
roboptim::Problem< F, boost::mpl::vector<> >::startingPoint_t
boost::optional< argument_t > startingPoint_t
Optional vector defines a starting point.
Definition: problem.hh:96
roboptim::SolverFactory::problem_t
T::problem_t problem_t
Problem type.
Definition: solver-factory.hh:62
roboptim::detail::LogJacobianConstraint::LogJacobianConstraint
LogJacobianConstraint(const_argument_ref x, const boost::filesystem::path &constraintPath)
Definition: optimization-logger.hxx:144
roboptim::ConstantFunction
GenericConstantFunction< EigenMatrixDense > ConstantFunction
Definition: fwd.hh:103
roboptim::CachedFunction::cachedFunctionJacobian
void cachedFunctionJacobian(jacobian_ref jacobian, const_argument_ref argument, typename detail::CachedFunctionTypes< U >::isDifferentiable_t::type *=0) const
Definition: cached-function.hxx:210
roboptim::GenericSumOfC1Squares::~GenericSumOfC1Squares
virtual ~GenericSumOfC1Squares()
Definition: sum-of-c1-squares.hxx:50
roboptim::detail::promote2::T_promote
T1 T_promote
Definition: autopromote.hh:157
roboptim::ParametrizedFunction::functionInputSize
size_type functionInputSize() const
Return the function's input size (i.e.
Definition: parametrized-function.hxx:51
autopromote.hh
roboptim::visualization::matplotlib::Import::Import
Import(const std::string &package)
Construct from a Python package.
Definition: matplotlib-commands.cc:33
roboptim::Problem< F, boost::mpl::vector<> >::scaling_t
std::vector< value_type > scaling_t
Scaling vector.
Definition: problem.hh:102
roboptim::GenericIdentityFunction::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref) const
Definition: identity.hh:73
roboptim::detail::ProductDifferentiation::Types::jacobian_ref
Product< U, V >::jacobian_ref jacobian_ref
Definition: product.hxx:68
roboptim::Cos::impl_hessian
void impl_hessian(hessian_ref hessian, const_argument_ref x, size_type) const
Hessian evaluation.
Definition: cos.hh:106
roboptim::Scalar::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: scalar.hxx:65
roboptim::DerivableParametrizedFunction::gradient
gradient_t gradient(const_argument_ref argument, size_type functionId=0, size_type order=0) const
Computes the gradient.
Definition: derivable-parametrized-function.hh:138
roboptim::Problem::argument_t
function_t::argument_t argument_t
Argument type.
Definition: problem.hh:301
roboptim::GenericFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: function.hh:568
function.hh
roboptim::Problem::intervals_t
function_t::intervals_t intervals_t
Intervals type.
Definition: problem.hh:316
roboptim::visualization::matplotlib::Command::isPlot_
bool isPlot_
Whether the command is a plot or not.
Definition: matplotlib-commands.hh:95
roboptim::Plus::Plus
Plus(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: plus.hxx:26
roboptim::BadJacobian::BadJacobian
BadJacobian(const_argument_ref x, const_jacobian_ref analyticalJacobian, const_jacobian_ref finiteDifferenceJacobian, const value_type &threshold)
Default constructor.
Definition: finite-difference-gradient.hxx:168
roboptim::SolverState::getParameter
const T & getParameter(const std::string &key) const
Get a parameter.
Definition: solver-state.hxx:154
roboptim::visualization::matplotlib::Command::command_
std::string command_
Store matplotlib command.
Definition: matplotlib-commands.hh:93
roboptim::GenericDummySolverLastState::setIterationCallback
virtual void setIterationCallback(callback_t callback)
Set the per-iteration callback.
Definition: dummy-laststate.hh:69
roboptim::GenericFunction::makeLowerInterval
static interval_t makeLowerInterval(value_type l)
Construct an interval from a lower bound.
Definition: function.hh:265
roboptim::SelectionById::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: selection-by-id.hxx:95
roboptim::detail::StructuredInputJacobianInternal< FuncType, roboptim::EigenMatrixSparse >::InputJacBlock
differentiableFunction_t::jacobian_ref InputJacBlock
input type of the jacobian given by the user
Definition: structured-input.hh:97
roboptim::CachedFunction::reset
void reset()
Reset the caches.
Definition: cached-function.hxx:99
deprecated.hh
roboptim::finiteDifferenceGradientPolicies::Policy::computeColumn
virtual void computeColumn(value_type epsilon, gradient_ref column, const_argument_ref argument, size_type colIdx, argument_ref xEps) const =0
roboptim::detail::CachedFunctionTypes::isNotTwiceDifferentiable_t
boost::disable_if< detail::derives_from_twice_differentiable_function< T > > isNotTwiceDifferentiable_t
Definition: cached-function.hxx:69
roboptim::SolverState::value_type
P::value_type value_type
Import value type from problem.
Definition: solver-state.hh:88
roboptim::fg::fail
std::ostream & fail(std::ostream &o)
Definition: terminal-color.hh:98
roboptim::Minus::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: minus.hxx:66
roboptim::CachedFunction::gradientCache_
std::vector< gradientCache_t > gradientCache_
Definition: cached-function.hh:170
roboptim::Polynomial::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref x, size_type) const
Definition: polynomial.hxx:142
roboptim::Solver::plugin_name_
std::string plugin_name_
Plugin name.
Definition: solver.hh:189
polynomial.hxx
ROBOPTIM_DLLEXPORT
#define ROBOPTIM_DLLEXPORT
Definition: portability.hh:47
roboptim::Polynomial::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: polynomial.hxx:122
roboptim::GenericNumericQuadraticFunction::GenericNumericQuadraticFunction
GenericNumericQuadraticFunction(const_symmetric_ref A, const_vector_ref b)
Build a quadratic function from a matrix and a vector.
Definition: numeric-quadratic-function.hxx:29
roboptim::GenericDifferentiableFunction::impl_jacobian
virtual void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Jacobian evaluation.
Definition: differentiable-function.hxx:70
roboptim::chain
boost::shared_ptr< Chain< U, V > > chain(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Chain two RobOptim functions.
Definition: chain.hh:126
roboptim::visualization::matplotlib::Import::command
const std::string & command() const
Return the import command.
Definition: matplotlib-commands.hh:56
roboptim::SolverState::constraintViolation
const boost::optional< value_type > & constraintViolation() const
Retrieve the current constraint violation.
Definition: solver-state.hxx:125
roboptim::Problem::size_type
function_t::size_type size_type
Size type.
Definition: problem.hh:304
roboptim::ParametrizedFunction::impl_compute
virtual result_t impl_compute(const_argument_ref argument) const =0
Function evaluation.
roboptim::Product
Product of two RobOptim functions.
Definition: fwd.hh:84
roboptim::Chain::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: chain.hxx:61
roboptim::GenericFunction::interval_t
std::pair< value_type, value_type > interval_t
Interval type (lower, upper).
Definition: function.hh:240
roboptim::detail::list_derives_from_function
Checks whether all the constraints derive from Function or SparseFunction.
Definition: utility.hh:271
plus.hxx
roboptim::NTimesDerivableFunction::size_type
Function::size_type size_type
Import size type from function.
Definition: n-times-derivable-function.hh:287
roboptim::GenericTwiceDifferentiableFunction::hessianSize_t
std::pair< size_type, size_type > hessianSize_t
Hessian size type represented as a pair of values.
Definition: twice-differentiable-function.hh:77
roboptim::BadGradient::finiteDifferenceGradient_
gradient_t finiteDifferenceGradient_
Gradient computed through finite differences.
Definition: decorator/finite-difference-gradient.hh:65
roboptim::detail::PromoteTrait::BOOST_MPL_ASSERT
BOOST_MPL_ASSERT((boost::mpl::bool_< knowBothRanks >))
roboptim::Problem< F, boost::mpl::vector<> >::intervals_t
function_t::intervals_t intervals_t
Definition: problem.hh:99
roboptim::OptimizationLogger::solverState_t
solver_t::solverState_t solverState_t
Definition: optimization-logger.hh:49
roboptim::Sin
Sin function.
Definition: sin.hh:31
roboptim::GenericFunction::operator()
result_t operator()(const_argument_ref argument) const
Evaluate the function at a specified point.
Definition: function.hh:458
roboptim::detail::StructuredInput
Provides utility methods to describe the input format of a function.
Definition: structured-input.hh:115
roboptim::GenericSolver::SOLVER_VALUE_WARNINGS
@ SOLVER_VALUE_WARNINGS
Solution has been found but some problems happened.
Definition: generic-solver.hh:58
roboptim::Map::origin
boost::shared_ptr< U > & origin()
Definition: map.hh:61
roboptim::detail::array_to_vector
ROBOPTIM_DLLAPI void array_to_vector(Function::vector_ref dst, const Function::value_type *src)
Definition: util.cc:45
roboptim::visualization::matplotlib::MatrixPlotType
Wrap enum for matrix plotting type.
Definition: matplotlib-matrix.hh:37
roboptim::CachedFunction
Store previous function computation.
Definition: cached-function.hh:59
roboptim::Plus::left
const boost::shared_ptr< U > & left() const
Definition: plus.hh:47
roboptim::Split
Select an element of a function's output.
Definition: split.hh:33
differentiable-function.hxx
roboptim::GenericNumericLinearFunction::b
const_vector_ref b() const
Definition: numeric-linear-function.hh:65
solver.hxx
roboptim::DummySolver
Dummy solver which always fails.
Definition: dummy.hh:34
roboptim::GenericSolver::solutions
solutions
Define the kind of solution which has been found.
Definition: generic-solver.hh:52
ROBOPTIM_STORAGE_ORDER
#define ROBOPTIM_STORAGE_ORDER
Definition: function.hh:112
roboptim::SolverFactory
Define a solver factory that instanciate the plug-ins.
Definition: fwd.hh:141
roboptim::detail::ProductDifferentiation::Types::vectorU_ref
U::vector_ref vectorU_ref
Definition: product.hxx:48
roboptim::Plus::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: plus.hxx:78
roboptim::LRUCache::~LRUCache
virtual ~LRUCache()
Destructor.
Definition: cache.hxx:36
roboptim::is_malloc_allowed
bool is_malloc_allowed()
Whether dynamic allocation is allowed.
Definition: alloc.hh:52
roboptim::finiteDifferenceEpsilon
static const double finiteDifferenceEpsilon
Default epsilon for finite difference class.
Definition: decorator/finite-difference-gradient.hh:34
roboptim::Problem::scalesVector
const scalesVect_t & scalesVector() const ROBOPTIM_CORE_DEPRECATED
Retrieve constraints scaling vector (deprecated version).
Definition: problem.hxx:484
roboptim::Sin::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref x) const
Definition: sin.hh:98
roboptim::GenericConstantFunction::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericLinearFunction< T >)
roboptim::NTimesDerivableFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: n-times-derivable-function.hxx:30
roboptim::finiteDifferenceGradientPolicies::Policy::computeGradient
virtual void computeGradient(value_type epsilon, gradient_ref gradient, const_argument_ref argument, size_type idFunction, argument_ref xEps) const =0
roboptim::Cos
Cos function.
Definition: cos.hh:31
roboptim::detail::StructuredInputJacobianInternal
This class gives access to the getJacobianBlock() method, whose implementation is to be specialized a...
Definition: structured-input.hh:47
roboptim::ParametrizedFunction::argument_t
F::argument_t argument_t
Import argument types.
Definition: parametrized-function.hh:67
roboptim::BadGradient::threshold_
value_type threshold_
Allowed threshold.
Definition: decorator/finite-difference-gradient.hh:74
roboptim::callback::Multiplexer
Callback multiplexer.
Definition: multiplexer.hh:40
roboptim::GenericDifferentiableFunction::ROBOPTIM_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_FUNCTION_FWD_TYPEDEFS_(GenericFunction< T >)
roboptim::Bind::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: bind.hxx:66
roboptim::Solver< F, boost::mpl::vector< F > >::problem
const problem_t & problem() const
Retrieve the problem.
Definition: solver.hxx:49
roboptim::NTimesDerivableFunction::derivabilityOrder
static const size_type derivabilityOrder
Function derivability order.
Definition: n-times-derivable-function.hh:290
roboptim::detail::get_descendant::BOOST_MPL_ASSERT_MSG
BOOST_MPL_ASSERT_MSG((boost::mpl::or_< boost::is_base_of< Type1, Type2 >, boost::is_base_of< Type2, Type1 > >::value), ONE_SHOULD_INHERIT_FROM_THE_OTHER,(Type1 &, Type2 &))
roboptim::visualization::Matplotlib::print
std::ostream & print(std::ostream &) const
Display the matplotlib script on the specified output stream.
Definition: matplotlib.cc:84
roboptim::ParametrizedFunction
Define an abstract parametrized mathematical function ( ).
Definition: parametrized-function.hh:52
roboptim::GenericFunction::makeInterval
static interval_t makeInterval(value_type l, value_type u)
Construct an interval from a lower and upper bound.
Definition: function.hh:249
roboptim::GenericQuadraticFunction
Define an abstract quadratic function.
Definition: fwd.hh:135
roboptim::SelectionById::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: selection-by-id.hxx:62
ROBOPTIM_GENERATE_TYPEDEFS_REF
#define ROBOPTIM_GENERATE_TYPEDEFS_REF(NAME, TYPE)
Definition: function.hh:63
gnuplot-commands.hh
roboptim::Problem::scaling_t
std::vector< value_type > scaling_t
Scaling vector.
Definition: problem.hh:319
roboptim::detail::ProductDifferentiation::Types::jacobian_t
Product< U, V >::jacobian_t jacobian_t
Definition: product.hxx:65
roboptim::detail::ProductDifferentiation::Types::gradientU_ref
U::gradient_ref gradientU_ref
Definition: product.hxx:59
roboptim::Plus::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: plus.hxx:55
roboptim::Result::outputSize
size_type outputSize
Output size (i.e. result size).
Definition: result.hh:65
roboptim::BadJacobian::finiteDifferenceJacobian_
gradient_t finiteDifferenceJacobian_
Jacobian computed through finite differences.
Definition: decorator/finite-difference-gradient.hh:115
roboptim::SolverError::lastState
const boost::optional< Result > & lastState() const
Retrieve the (optional) last state of the solver.
Definition: solver-error.cc:53
roboptim::Polynomial::applyPolynomial
value_type applyPolynomial(const_vector_ref coeffs, const_argument_ref x) const
Implement Horner's method.
Definition: polynomial.hxx:129
roboptim::DerivableParametrizedFunction::gradientSize
size_type gradientSize() const
Return the gradient size.
Definition: derivable-parametrized-function.hh:70
roboptim::detail::EvaluateConstraintViolation::value_type
P::value_type value_type
Definition: optimization-logger.hxx:72
roboptim::Product::right
V & right()
Definition: product.hh:62
roboptim::visualization::matplotlib::MatrixPlotType::Type
Type
Plotting type for matrices.
Definition: matplotlib-matrix.hh:40
roboptim::finiteDifferenceGradientPolicies::Policy::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericDifferentiableFunction< T >)
roboptim::FunctionPool::impl_compute
virtual void impl_compute(result_ref result, const_argument_ref x) const
Definition: function-pool.hxx:193
roboptim::FunctionPool::pool_t
F pool_t
Function type of the pool.
Definition: function-pool.hh:66
split.hh
roboptim::detail::StateParameterPrintVisitor::StateParameterPrintVisitor
StateParameterPrintVisitor(std::ostream &o)
Definition: solver-state.hxx:54
roboptim::CachedFunction::~CachedFunction
~CachedFunction()
Definition: cached-function.hxx:93
roboptim::Scalar::~Scalar
~Scalar()
Definition: scalar.hxx:39
getTypeIdOfConstraintsList
const ROBOPTIM_DLLEXPORT char * getTypeIdOfConstraintsList()
Definition: dummy-td.cc:75
roboptim::ResultWithWarnings::print
virtual std::ostream & print(std::ostream &o) const
Display the result on the specified output stream.
Definition: result-with-warnings.cc:39
roboptim::Minus::Minus
Minus(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: minus.hxx:26
roboptim::Parameter::Parameter
ROBOPTIM_DLLAPI Parameter()
Default constructor.
Definition: solver.cc:49
roboptim::visualization::gnuplot::reread
ROBOPTIM_DLLAPI Command reread()
Make a Gnuplot reread command.
roboptim::resetindent
ROBOPTIM_DLLAPI std::ostream & resetindent(std::ostream &o)
Reset the indentation.
Definition: indent.cc:48
roboptim::GenericIdentityFunction::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericLinearFunction< T >)
roboptim::Minus
Subtract two RobOptim functions.
Definition: fwd.hh:80
roboptim::BadGradient::maxDeltaComponent_
size_type maxDeltaComponent_
Component containing the maximum error.
Definition: decorator/finite-difference-gradient.hh:71
roboptim::finiteDifferenceGradientPolicies::Simple::computeColumn
void computeColumn(value_type epsilon, gradient_ref column, const_argument_ref argument, size_type colIdx, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:524
roboptim::detail::derives_from_twice_differentiable_function
Checks whether the function type derives from TwiceDifferentiableFunction or TwiceDifferentiableSpars...
Definition: utility.hh:252
roboptim::detail::const_eigen_ref::type
const typedef Eigen::Ref< const T > & type
Definition: utility.hh:108
roboptim::Bind::origin
const boost::shared_ptr< U > & origin() const
Definition: bind.hh:55
sum-of-c1-squares.hxx
roboptim::checkGradientAndThrow
void checkGradientAndThrow(const GenericDifferentiableFunction< T > &function, typename GenericDifferentiableFunction< T >::size_type functionId, typename GenericDifferentiableFunction< T >::const_argument_ref x, typename GenericDifferentiableFunction< T >::value_type threshold=finiteDifferenceThreshold)
Definition: finite-difference-gradient.hxx:281
roboptim::Parameter::vector_t
Function::vector_t vector_t
Definition: solver.hh:48
roboptim::GenericFiniteDifferenceGradient::adaptee_
const GenericFunction< T > & adaptee_
Reference to the wrapped function.
Definition: decorator/finite-difference-gradient.hh:330
roboptim::LRUCache::operator[]
V & operator[](const_key_ref key)
Access a cached element.
Definition: cache.hxx:174
roboptim::visualization::matplotlib::Import
matplotlib import.
Definition: matplotlib-commands.hh:39
roboptim::Minus::MinusShPtr_t
boost::shared_ptr< Minus > MinusShPtr_t
Definition: minus.hh:42
roboptim::SelectionById
Select part of a function.
Definition: selection-by-id.hh:36
roboptim::SolverState::parameters_
parameters_t parameters_
Solver state extra parameters (solver-specific parameters etc.).
Definition: solver-state.hh:166
roboptim::GenericFiniteDifferenceGradient::GenericFiniteDifferenceGradient
GenericFiniteDifferenceGradient(const GenericFunction< T > &f, value_type e=finiteDifferenceEpsilon)
Instantiate a finite differences gradient.
Definition: finite-difference-gradient.hxx:117
roboptim::visualization::matplotlib::plot_mat
ROBOPTIM_DLLAPI Command plot_mat(GenericFunctionTraits< EigenMatrixDense >::const_matrix_ref mat, MatrixPlotType::Type type=MatrixPlotType::Values)
Plot the structure of a matrix with matplotlib.
Definition: matplotlib-matrix.cc:179
roboptim::Sin::Sin
Sin()
Build an constant function.
Definition: sin.hh:40
roboptim::Split::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericTwiceDifferentiableFunction< traits_t >)
roboptim::Chain::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: chain.hxx:83
dummy-td.hh
roboptim::Selection::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::NoSolution
Tag a result if no solution has been found.
Definition: fwd.hh:29
roboptim::Minus::parentType_t
detail::PromoteTrait< U, V >::T_promote parentType_t
Definition: minus.hh:39
roboptim::detail::cast_constraint_type
Get the constraint type of CLIST that best match C.
Definition: utility.hh:213
roboptim::DummySolverTd::DummySolverTd
DummySolverTd(const problem_t &problem)
Build a solver from a problem.
Definition: dummy-td.cc:28
roboptim::SolverState::argument_t
P::function_t::argument_t argument_t
Import argument types from problem.
Definition: solver-state.hh:85
roboptim::GenericNumericLinearFunction::impl_gradient
void impl_gradient(gradient_ref, const_argument_ref, size_type=0) const
Definition: numeric-linear-function.hxx:98
solver-warning.hh
roboptim::SumOfC1SquaresSparse
GenericSumOfC1Squares< EigenMatrixSparse > SumOfC1SquaresSparse
Sum of the squares of sparse differentiable functions.
Definition: sum-of-c1-squares.hh:94
roboptim::detail::ProductDifferentiation::Types::rowVectorV_ref
V::rowVector_ref rowVectorV_ref
Definition: product.hxx:54
roboptim::detail::ProductDifferentiation::Types::jacobianV_t
V::jacobian_t jacobianV_t
Definition: product.hxx:64
roboptim::GenericDifferentiableFunction
Define an abstract derivable function ( ).
Definition: differentiable-function.hh:79
roboptim::detail::StructuredInputJacobianInternal< FuncType, roboptim::EigenMatrixDense >::InputJacBlock
differentiableFunction_t::jacobian_ref InputJacBlock
input type of the jacobian given by the user
Definition: structured-input.hh:62
roboptim::GenericNumericQuadraticFunction::c
const_vector_ref c() const
Definition: numeric-quadratic-function.hh:87
roboptim::Concatenate::right
const boost::shared_ptr< U > & right() const
Definition: concatenate.hh:70
roboptim::detail::PromoteTrait
Definition: autopromote.hh:167
roboptim::detail::ProductDifferentiation::Types::jacobianU_t
U::jacobian_t jacobianU_t
Definition: product.hxx:63
roboptim::GenericSumOfC1Squares
Generic sum of the squares of differentiable functions.
Definition: sum-of-c1-squares.hh:44
roboptim::Map
Apply a function several times to an input vector.
Definition: map.hh:42
roboptim::DerivableParametrizedFunction::const_jacobian_ref
F::const_jacobian_ref const_jacobian_ref
Definition: derivable-parametrized-function.hh:61
roboptim::CachedFunction::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericTwiceDifferentiableFunction< traits_t >)
roboptim::OptimizationLogger::solver_t
T solver_t
Definition: optimization-logger.hh:44
roboptim::visualization::gnuplot::detail::sparse_jacobian_to_gnuplot
std::string sparse_jacobian_to_gnuplot(DifferentiableSparseFunction::const_jacobian_ref jac, const std::string &name)
Definition: gnuplot-differentiable-function.cc:47
optimization-logger.hxx
roboptim::finiteDifferenceGradientPolicies::Simple::computeGradient
void computeGradient(value_type epsilon, gradient_ref gradient, const_argument_ref argument, size_type idFunction, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:483
roboptim::LRUCache::cbegin
const_iterator cbegin() const
Iterator to the beginning of the cache.
Definition: cache.hxx:154
roboptim::detail::EvaluateConstraintViolation
Definition: optimization-logger.hxx:69
roboptim::Parameter::value
parameterValues_t value
Value.
Definition: solver.hh:66
roboptim::GenericTwiceDifferentiableFunction
Define an abstract function which is twice-derivable ( ).
Definition: fwd.hh:123
roboptim::QuadraticFunction
GenericQuadraticFunction< EigenMatrixDense > QuadraticFunction
Definition: fwd.hh:135
indent.hh
concatenate.hxx
roboptim::DummyDifferentiableSparseSolverLastState
GenericDummySolverLastState< DifferentiableSparseFunction > DummyDifferentiableSparseSolverLastState
Definition: dummy-laststate.hh:88
roboptim::detail::ProductDifferentiation::gradient
static void gradient(typename Types< U, V >::gradient_ref grad_uv, const typename Types< U, V >::rowVectorU_ref u, const typename Types< U, V >::rowVectorV_ref v, const typename Types< U, V >::gradientU_ref grad_u, const typename Types< U, V >::gradientV_ref grad_v, typename boost::enable_if< typename Types< U, V >::fullDense_t >::type *=0)
Full dense version of gradient computation.
Definition: product.hxx:75
roboptim::NTimesDerivableFunction::~NTimesDerivableFunction
virtual ~NTimesDerivableFunction()
Definition: n-times-derivable-function.hxx:24
selection.hh
roboptim::detail::PrintSolverParameter::operator()
std::ostream & operator()(const T &val)
Definition: solver.cc:32
roboptim::Solver::logger
static log4cxx::LoggerPtr logger
Pointer to function logger (see log4cxx documentation).
Definition: solver.hh:192
derivable-parametrized-function.hh
roboptim::visualization::gnuplot::set
ROBOPTIM_DLLAPI Command set(const char *var, const char *val="")
Make a Gnuplot set command.
Definition: gnuplot-commands.cc:84
roboptim::CachedFunction::impl_hessian
virtual void impl_hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
Definition: cached-function.hxx:307
roboptim::Chain::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: chain.hxx:70
roboptim::minus
boost::shared_ptr< Minus< U, V > > minus(boost::shared_ptr< U > left, boost::shared_ptr< V > right)
Definition: minus.hh:88
roboptim::FunctionPool::~FunctionPool
virtual ~FunctionPool()
Virtual destructor.
Definition: function-pool.hxx:188
roboptim::detail::ProductDifferentiation::Types::rowVectorU_t
U::rowVector_t rowVectorU_t
Definition: product.hxx:51
roboptim::visualization::operator<<
Gnuplot & operator<<(Gnuplot &gp, T t)
Override operator<< to handle Gnuplot command insertion.
Definition: gnuplot.hh:115
roboptim::CachedFunction::cacheKey_t
argument_t cacheKey_t
Key type for the cache.
Definition: cached-function.hh:73
roboptim::visualization::Matplotlib::resetImports
void resetImports()
Reset imports to initial values.
Definition: matplotlib.cc:63
roboptim::OptimizationLogger::jacobian_t
function_t::matrix_t jacobian_t
Definition: optimization-logger.hh:54
roboptim::visualization::gnuplot::Command::~Command
~Command()
Definition: gnuplot-commands.cc:36
roboptim::Problem::function
const function_t & function() const
Retrieve cost function.
Definition: problem.hxx:309
roboptim::Problem::print
std::ostream & print(std::ostream &o) const
Display the problem on the specified output stream.
Definition: problem.hxx:624
roboptim::GenericDifferentiableFunction::jacobian
jacobian_t jacobian(const_argument_ref argument) const
Computes the jacobian.
Definition: differentiable-function.hh:131
roboptim::visualization::gnuplot::plot_mat
Command plot_mat(typename GenericFunctionTraits< T >::const_matrix_ref)
Definition: gnuplot-matrix.hh:61
scalar.hh
roboptim::addNonScalarConstraint
void addNonScalarConstraint(P &problem, boost::shared_ptr< C > constraint, std::vector< Function::interval_t > interval, std::vector< Function::value_type > scale=std::vector< Function::value_type >())
Definition: split.hxx:156
roboptim::Minus::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: minus.hxx:55
roboptim::Split::impl_gradient
virtual void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: split.hxx:78
ROBOPTIM_GENERATE_TYPEDEFS_EIGEN_REF
#define ROBOPTIM_GENERATE_TYPEDEFS_EIGEN_REF(NAME, TYPE)
Definition: function.hh:51
roboptim::ResultWithWarnings::~ResultWithWarnings
~ResultWithWarnings()
Definition: result-with-warnings.cc:47
roboptim::detail::add_shared_ptr::type
boost::mpl::transform< CLIST, typename boost::shared_ptr< boost::mpl::_1 > >::type type
Result.
Definition: utility.hh:60
roboptim::Minus::left
const boost::shared_ptr< U > & left() const
Definition: minus.hh:47
destroy
ROBOPTIM_DLLEXPORT void destroy(solver_t *p)
Definition: dummy.cc:85
roboptim::Minus::right
const boost::shared_ptr< V > & right() const
Definition: minus.hh:57
identity.hh
roboptim::Solver::Solver
Solver(const problem_t &problem)
Instantiate a solver from a problem.
Definition: solver.hxx:26
roboptim::finiteDifferenceGradientPolicies::Simple::computeJacobian
void computeJacobian(value_type epsilon, jacobian_ref jacobian, const_argument_ref argument, argument_ref xEps) const
Definition: finite-difference-gradient.hxx:541
roboptim::Scalar::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: scalar.hxx:54
roboptim::CachedFunction::function_
boost::shared_ptr< T > function_
Definition: cached-function.hh:168
roboptim::Problem::argumentScaling
scaling_t & argumentScaling()
Retrieve arguments scaling.
Definition: problem.hxx:491
debug.hh
roboptim::callback::Multiplexer::unregister
void unregister()
Unregister the multiplexer from the solver.
Definition: multiplexer.hxx:116
roboptim::SelectionById::parentType_t
detail::AutopromoteTrait< U >::T_type parentType_t
Definition: selection-by-id.hh:39
roboptim::GenericSolver::SOLVER_VALUE
@ SOLVER_VALUE
Solution has been found.
Definition: generic-solver.hh:56
roboptim::GenericSolver::solve
virtual void solve()=0
Solve the problem.
roboptim::Result::value
vector_t value
Function value at the solver found point.
Definition: result.hh:69
roboptim::Polynomial::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericTwiceDifferentiableFunction< T >)
roboptim::Derivative::DerivativeShPtr_t
boost::shared_ptr< Derivative > DerivativeShPtr_t
Definition: derivative.hh:70
roboptim::SolverState::SolverState
SolverState(const problem_t &problem)
Instantiate a solver from a problem.
Definition: solver-state.hxx:81
roboptim::GenericDifferentiableFunction::jacobian
void jacobian(jacobian_ref jacobian, const_argument_ref argument) const
Computes the jacobian.
Definition: differentiable-function.hh:145
linear-function.hxx
roboptim::Map::origin
const boost::shared_ptr< U > & origin() const
Definition: map.hh:56
roboptim::detail::get_descendant
Get the descendant among two relatives.
Definition: utility.hh:186
roboptim::detail::EvaluateConstraintViolation::vector_t
P::vector_t vector_t
Definition: optimization-logger.hxx:71
roboptim::GenericSolver::minimum
const result_t & minimum()
Returns the function minimum This solves the problem automatically, if it has not yet been solved.
Definition: generic-solver.cc:53
roboptim::Derivative::Derivative
Derivative(boost::shared_ptr< U > origin, size_type variableId)
Derivative operator constructor.
Definition: derivative.hh:75
roboptim::Problem::ROBOPTIM_CORE_DEPRECATED
scaling_t scales_t ROBOPTIM_CORE_DEPRECATED
Scaling vector (deprecated typedef)
Definition: problem.hh:322
roboptim::OptimizationLogger::function_t
solver_t::problem_t::function_t function_t
Definition: optimization-logger.hh:53
roboptim::GenericFunction::getUpperBound
static value_type getUpperBound(const discreteInterval_t &interval)
Get the upper bound of a discrete interval.
Definition: function.hh:346
roboptim::Problem< F, boost::mpl::vector<> >::interval_t
function_t::interval_t interval_t
Definition: problem.hh:98
roboptim::detail::CachedFunctionTypes::isNTimesDerivable_t
boost::enable_if< detail::derives_from_twice_differentiable_function< T > > isNTimesDerivable_t
Definition: cached-function.hxx:73
roboptim::visualization::Gnuplot::~Gnuplot
~Gnuplot()
Definition: gnuplot.cc:38
roboptim::Problem::addConstraint
void addConstraint(boost::shared_ptr< C > constraint, interval_t interval, value_type scale=1.)
Add a constraint to the problem.
Definition: problem.hxx:331
ASSERT_CONSTRAINT_TYPE
#define ASSERT_CONSTRAINT_TYPE(C, CLIST)
Definition: problem.hxx:322
roboptim::Problem::~Problem
virtual ~Problem()
Virtual destructor.
Definition: problem.hxx:254
roboptim::SolverWarning
Exception used for non-critical errors during optimization.
Definition: solver-warning.hh:34
roboptim::Result::size_type
Function::size_type size_type
Import size type from Function class.
Definition: result.hh:43
roboptim::Derivative
Return the derivative of a function w.r.t.
Definition: derivative.hh:34
GNUPLOT_STR_COMMAND
#define GNUPLOT_STR_COMMAND(NAME, ARG)
Definition: gnuplot-commands.cc:53
roboptim::CachedFunction::impl_gradient
virtual void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: cached-function.hxx:200
roboptim::GenericNumericLinearFunction::A
matrix_ref A()
Definition: numeric-linear-function.hh:70
roboptim::Bind::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: bind.hxx:80
roboptim::NTimesDerivableFunction< 2 >::NTimesDerivableFunction
NTimesDerivableFunction(size_type outputSize=1, std::string name=std::string())
Concrete class constructor should call this constructor.
Definition: n-times-derivable-function.hh:169
roboptim::OptimizationLogger::solver
const solver_t & solver() const
Return the solver associated with the logger.
Definition: optimization-logger.hxx:568
roboptim::detail::PrecisionTrait::knowPrecisionRank
@ knowPrecisionRank
Definition: autopromote.hh:33
roboptim::Parameter::value_type
Function::value_type value_type
Definition: solver.hh:47
solver-factory.hxx
roboptim::GenericNumericQuadraticFunction::const_symmetric_ref
const_matrix_ref const_symmetric_ref
Definition: numeric-quadratic-function.hh:46
roboptim::detail::row_vector_stride
Get the matrix stride type for a row vector, given a matrix storage order.
Definition: utility.hh:144
roboptim::GenericFiniteDifferenceGradient
Compute automatically a gradient with finite differences.
Definition: decorator/finite-difference-gradient.hh:301
roboptim::Sin::ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(GenericTwiceDifferentiableFunction< T >)
roboptim::LRUCache::hash_function
hash_t hash_function(const_key_ref key) const
Hash function used in the cache.
Definition: cache.hxx:209
roboptim::DerivableParametrizedFunction::gradient_ref
F::gradient_ref gradient_ref
Definition: derivable-parametrized-function.hh:56
cached-function.hh
roboptim::OptimizationLogger::size_type
solver_t::problem_t::size_type size_type
Definition: optimization-logger.hh:47
roboptim::StateParameter
Solver state parameters type.
Definition: solver-state.hh:44
roboptim::callback::Multiplexer::~Multiplexer
virtual ~Multiplexer()
Virtual destructor.
Definition: multiplexer.hxx:46
roboptim::Chain
Chain two RobOptim functions.
Definition: chain.hh:48
roboptim::Concatenate::impl_gradient
void impl_gradient(gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
Definition: concatenate.hxx:92
roboptim::Concatenate::right
boost::shared_ptr< U > & right()
Definition: concatenate.hh:75
constant.hh
roboptim::detail::printConstraint::printConstraint
printConstraint(std::ostream &o, const P &problem, std::size_t i)
Definition: problem.hxx:560
roboptim::Sin::impl_compute
void impl_compute(result_ref result, const_argument_ref x) const
Definition: sin.hh:58
roboptim::GenericTwiceDifferentiableFunction::ROBOPTIM_GENERATE_TRAITS_REFS_
ROBOPTIM_GENERATE_TRAITS_REFS_(hessian)
Hessian type.
roboptim::Selection::parentType_t
detail::AutopromoteTrait< U >::T_type parentType_t
Definition: selection.hh:40
roboptim::Plus::parentType_t
detail::PromoteTrait< U, V >::T_promote parentType_t
Definition: plus.hh:39
roboptim::DerivableParametrizedFunction::result_t
F result_t
Import result type.
Definition: derivable-parametrized-function.hh:50
result-with-warnings.hh
roboptim::SolverFactory::operator()
solver_t & operator()()
Retrieve a reference on the solver.
Definition: solver-factory.hxx:239
roboptim::Result::x
vector_t x
Point found by the solver.
Definition: result.hh:67
roboptim::Polynomial::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref x) const
Definition: polynomial.hxx:152
roboptim::NTimesDerivableFunction< 2 >::derivative
void derivative(derivative_ref derivative, value_type argument, size_type order=1) const
Compute the derivative of the function.
Definition: n-times-derivable-function.hh:143
roboptim::QuadraticSparseFunction
GenericQuadraticFunction< EigenMatrixSparse > QuadraticSparseFunction
Definition: fwd.hh:137
roboptim::visualization::Matplotlib::Matplotlib
Matplotlib(std::pair< int, int > multiplot=std::make_pair(1, 1), bool with_header=true)
Default constructor can not be called directly.
Definition: matplotlib.cc:31
roboptim::selectionById
boost::shared_ptr< SelectionById< U > > selectionById(boost::shared_ptr< U > origin, std::vector< bool > selector)
Definition: selection-by-id.hh:78
twice-derivable-function.hh
roboptim::visualization::Matplotlib::push_command
void push_command(const matplotlib::Command &cmd)
Add a new matplotlib command to the script.
Definition: matplotlib.cc:72
roboptim::scalar
boost::shared_ptr< Scalar< U > > scalar(boost::shared_ptr< U > origin, typename Scalar< U >::size_type start=0, typename Scalar< U >::size_type size=1)
Definition: scalar.hh:78
roboptim::allclose
ROBOPTIM_DLLAPI bool allclose(const Eigen::SparseMatrix< double > &a, const Eigen::SparseMatrix< double > &b, double rtol=Eigen::NumTraits< double >::dummy_precision(), double atol=Eigen::NumTraits< double >::epsilon())
Compare sparse vectors (matrices) using both relative and absolute tolerances.
Definition: util.cc:104
roboptim::Selection::~Selection
~Selection()
Definition: selection.hxx:50
roboptim::visualization::Matplotlib::clear
void clear()
Clear the vectors of imports and commands.
Definition: matplotlib.cc:162
roboptim::ParametrizedFunction::value_type
F::value_type value_type
Import value type.
Definition: parametrized-function.hh:57
structured-input.hxx
roboptim::OptimizationLogger::traits_t
solver_t::problem_t::function_t::traits_t traits_t
Definition: optimization-logger.hh:51
solver_t
DummySolverLastState::parent_t solver_t
Definition: dummy-laststate.cc:29
portability.hh
roboptim::Minus::impl_jacobian
void impl_jacobian(jacobian_ref jacobian, const_argument_ref arg) const
Definition: minus.hxx:78
roboptim::GenericIdentityFunction::impl_compute
void impl_compute(result_ref result, const_argument_ref argument) const
Definition: identity.hh:65
roboptim::GenericDummySolverLastState::callback
const callback_t & callback() const
Definition: dummy-laststate.hh:74
roboptim::LRUCache::key_t
K key_t
Type of keys.
Definition: cache.hh:45
roboptim::SolverState::cost_
boost::optional< value_type > cost_
Current cost.
Definition: solver-state.hh:158
roboptim::Product::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
roboptim::StateParameter::print
virtual std::ostream & print(std::ostream &) const
Display the solver parameter on the specified output stream.
Definition: solver-state.hxx:72
roboptim::Concatenate::left
const boost::shared_ptr< U > & left() const
Definition: concatenate.hh:60
roboptim::Split::impl_hessian
virtual void impl_hessian(hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
Definition: split.hxx:110
roboptim::visualization::gnuplot::quit
ROBOPTIM_DLLAPI Command quit()
Make a Gnuplot quit command.
roboptim::LRUCache::LRUCache
LRUCache(size_t size=10)
Constructor.
Definition: cache.hxx:26
roboptim::Chain::ChainShPtr_t
boost::shared_ptr< Chain > ChainShPtr_t
Definition: chain.hh:54
roboptim::GenericNumericQuadraticFunction::impl_jacobian
void impl_jacobian(jacobian_ref, const_argument_ref) const
Definition: numeric-quadratic-function.hxx:101
solver-state.hh
roboptim::Concatenate::left
boost::shared_ptr< U > & left()
Definition: concatenate.hh:65
roboptim::detail::StructuredInputJacobianInternal< FuncType, roboptim::EigenMatrixSparse >::differentiableFunction_t
roboptim::GenericDifferentiableFunction< typename FuncType::traits_t > differentiableFunction_t
Differentiable function type.
Definition: structured-input.hh:77
roboptim::GenericDifferentiableFunction::gradientSize
size_type gradientSize() const
Return the gradient size.
Definition: differentiable-function.hh:96
roboptim::Function
GenericFunction< EigenMatrixDense > Function
Dense function.
Definition: fwd.hh:65
split.hxx
roboptim::Problem::constraints_t
std::vector< constraint_t > constraints_t
Constraints are represented as a vector of constraints.
Definition: problem.hh:307
roboptim::Cos::~Cos
~Cos()
Definition: cos.hh:45
roboptim::GenericDummySolverLastState::callback_t
parent_t::callback_t callback_t
Callback function type.
Definition: dummy-laststate.hh:52
roboptim::OptimizationLogger::perIterationCallback
void perIterationCallback(const problem_t &pb, const solverState_t &state)
Definition: optimization-logger.hxx:459
roboptim::ParametrizedFunction::~ParametrizedFunction
virtual ~ParametrizedFunction()
Definition: parametrized-function.hh:108
roboptim::Minus::left
U & left()
Definition: minus.hh:52
roboptim::GenericDummySolverLastState::solverState_
solverState_t solverState_
Current state of the solver (used by the callback function).
Definition: dummy-laststate.hh:83
roboptim::detail::ConcatenateTypes::traits_t
T::traits_t traits_t
Definition: concatenate.hxx:33
roboptim::CachedFunction::cachedFunctionHessian
void cachedFunctionHessian(hessian_ref hessian, const_argument_ref argument, size_type functionId, typename detail::CachedFunctionTypes< U >::isTwiceDifferentiable_t::type *=0) const
Definition: cached-function.hxx:260
roboptim::Chain::ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_
ROBOPTIM_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_(parentType_t)
fwd.hh
roboptim::ParametrizedFunction::print
virtual std::ostream & print(std::ostream &) const
Display the function on the specified output stream.
Definition: parametrized-function.hxx:66
roboptim::detail::LogJacobianConstraint::traits_t
problem_t::function_t::traits_t traits_t
Function traits.
Definition: optimization-logger.hxx:129
roboptim::OptimizationLogger::append
void append(const std::string &text)
Append extra information to the log file.
Definition: optimization-logger.hxx:329
roboptim::GenericNumericLinearFunction
Build a linear function from a vector and a matrix.
Definition: fwd.hh:93
roboptim::detail::ConvertConstraint
Convert a constraint to a proper type.
Definition: utility.hh:313
roboptim::GenericFunction::logger
static log4cxx::LoggerPtr logger
Pointer to function logger (see log4cxx documentation).
Definition: function.hh:542
roboptim::GenericNumericQuadraticFunction::A
const_matrix_ref A() const
Definition: numeric-quadratic-function.hh:77
roboptim::detail::promote2
Definition: autopromote.hh:155
roboptim::detail::EvaluateConstraintViolation::intervalsVect_t
P::intervalsVect_t intervalsVect_t
Definition: optimization-logger.hxx:74
roboptim::GenericFunction::value_type
GenericFunctionTraits< T >::value_type value_type
Values type.
Definition: function.hh:173
roboptim::GenericQuadraticFunction::parent_t
GenericTwiceDifferentiableFunction< T > parent_t
Definition: quadratic-function.hh:38
roboptim::Product::~Product
~Product()
Definition: product.hxx:233
roboptim::visualization::matplotlib::detail::sparse_matrix_to_matplotlib
std::string sparse_matrix_to_matplotlib(GenericFunctionTraits< EigenMatrixSparse >::const_matrix_ref mat, MatrixPlotType::Type type)
Definition: matplotlib-matrix.cc:119
roboptim::finiteDifferenceGradientPolicies::FivePointsRule::FivePointsRule
FivePointsRule(const GenericFunction< T > &adaptee)
Definition: decorator/finite-difference-gradient.hh:245
roboptim::detail::PrintSolverParameter
Definition: solver.cc:26
roboptim::detail::EvaluateConstraint
Definition: optimization-logger.hxx:40
create
ROBOPTIM_DLLEXPORT solver_t * create(const DummySolverLastState::problem_t &pb)
Definition: dummy-laststate.cc:48
roboptim::NTimesDerivableFunction< 2 >::parent_t
TwiceDifferentiableFunction parent_t
Parent type.
Definition: n-times-derivable-function.hh:55
roboptim::detail::AutopromoteTrait< Scalar< U > >::T_type
Scalar< U >::parent_t T_type
Definition: autopromote.hh:106
roboptim::Product::left
const boost::shared_ptr< U > & left() const
Definition: product.hh:47
roboptim::CachedFunction::gradientCache_t
LRUCache< cacheKey_t, gradient_t, Hasher > gradientCache_t
Definition: cached-function.hh:76
bind.hh