Ceres

class Ceres(*args)

Interface to Ceres Solver.

This class exposes the solvers from the non-linear least squares optimization library [ceres2012].

More details about least squares algorithms are available here.

Algorithms are also available for general unconstrained optimization.

Parameters:
problemOptimizationProblem

Optimization problem to solve, either least-squares or general (unconstrained).

algoNamestr

The identifier of the algorithm. Use GetAlgorithmNames() to list available names.

Notes

Solvers use first order derivative information.

As for constraint support, only the trust-region solvers allow for bound constraints:

Algorithm

Method type

Problem type support

Constraint support

LEVENBERG_MARQUARDT

trust-region

least-squares

bounds

DOGLEG

trust-region

least-squares

bounds

STEEPEST_DESCENT

line-search

least-squares, general

none

NONLINEAR_CONJUGATE_GRADIENT

line-search

least-squares, general

none

LBFGS

line-search

least-squares, general

none

BFGS

line-search

least-squares, general

none

Ceres least squares solver can be further tweaked thanks to the following ResourceMap parameters, refer to nlls solver options for more details.

Key

Type

Ceres-minimizer_type

str

Ceres-line_search_direction_type

str

Ceres-line_search_type

str

Ceres-nonlinear_conjugate_gradient_type

str

Ceres-max_lbfgs_rank

int

Ceres-use_approximate_eigenvalue_bfgs_scaling

bool

Ceres-line_search_interpolation_type

str

Ceres-min_line_search_step_size

float

Ceres-line_search_sufficient_function_decrease

float

Ceres-max_line_search_step_contraction

float

Ceres-min_line_search_step_contraction

float

Ceres-max_num_line_search_step_size_iterations

int

Ceres-max_num_line_search_direction_restarts

int

Ceres-line_search_sufficient_curvature_decrease

float

Ceres-max_line_search_step_expansion

float

Ceres-trust_region_strategy_type

str

Ceres-dogleg_type

str

Ceres-use_nonmonotonic_steps

bool

Ceres-max_consecutive_nonmonotonic_steps

int

Ceres-max_num_iterations

int

Ceres-max_solver_time_in_seconds

float

Ceres-num_threads

int

Ceres-initial_trust_region_radius

float

Ceres-max_trust_region_radius

float

Ceres-min_trust_region_radius

float

Ceres-min_relative_decrease

float

Ceres-min_lm_diagonal

float

Ceres-max_lm_diagonal

float

Ceres-max_num_consecutive_invalid_steps

int

Ceres-function_tolerance

float

Ceres-gradient_tolerance

float

Ceres-parameter_tolerance

float

Ceres-preconditioner_type

str

Ceres-visibility_clustering_type

str

Ceres-dense_linear_algebra_library_type

str

Ceres-sparse_linear_algebra_library_type

str

Ceres-use_explicit_schur_complement

bool

Ceres-dynamic_sparsity

bool

Ceres-min_linear_solver_iterations

int

Ceres-max_linear_solver_iterations

int

Ceres-eta

float

Ceres-jacobi_scaling

bool

Ceres-use_inner_iterations

bool

Ceres-inner_iteration_tolerance

float

Ceres-logging_type

str

Ceres-minimizer_progress_to_stdout

bool

Ceres-trust_region_problem_dump_directory

str

Ceres-trust_region_problem_dump_format_type

str

Ceres-check_gradients

bool

Ceres-gradient_check_relative_precision

float

Ceres-gradient_check_numeric_derivative_relative_step_size

float

Ceres-update_state_every_iteration

bool

Ceres unconstrained solver can be further tweaked using the following ResourceMap parameters, refer to gradient solver options for more details.

Key

Type

Ceres-line_search_direction_type

str

Ceres-line_search_type

str

Ceres-nonlinear_conjugate_gradient_type

str

Ceres-max_lbfgs_rank

int

Ceres-use_approximate_eigenvalue_bfgs_scaling

bool

Ceres-line_search_interpolation_type

str

Ceres-min_line_search_step_size

float

Ceres-line_search_sufficient_function_decrease

float

Ceres-max_line_search_step_contraction

float

Ceres-min_line_search_step_contraction

float

Ceres-max_num_line_search_step_size_iterations

int

Ceres-max_num_line_search_direction_restarts

int

Ceres-line_search_sufficient_curvature_decrease

float

Ceres-max_line_search_step_expansion

float

Ceres-max_num_iterations

int

Ceres-max_solver_time_in_seconds

float

Ceres-function_tolerance

float

Ceres-gradient_tolerance

float

Ceres-parameter_tolerance

float

Ceres-logging_type

str

Ceres-minimizer_progress_to_stdout

bool

Examples

List available algorithms:

>>> import openturns as ot
>>> print(ot.Ceres.GetAlgorithmNames())
[LEVENBERG_MARQUARDT,DOGLEG,...

Solve a least-squares problem:

>>> dim = 2
>>> residualFunction = ot.SymbolicFunction(['x0', 'x1'], ['10*(x1-x0^2)', '1-x0'])
>>> problem = ot.LeastSquaresProblem(residualFunction)
>>> problem.setBounds(ot.Interval([-3.0] * dim, [5.0] * dim))
>>> ot.ResourceMap.AddAsScalar('Ceres-gradient_tolerance', 1e-5)  
>>> algo = ot.Ceres(problem, 'LEVENBERG_MARQUARDT')  
>>> algo.setStartingPoint([0.0] * dim)  
>>> algo.run()  
>>> result = algo.getResult()  
>>> x_star = result.getOptimalPoint()  
>>> y_star = result.getOptimalValue()  

Or, solve a general optimization problem:

>>> dim = 4
>>> linear = ot.SymbolicFunction(['x1', 'x2', 'x3', 'x4'], ['(x1-1)^2+(x2-2)^2+(x3-3)^2+(x4-4)^2'])
>>> problem = ot.OptimizationProblem(linear)
>>> ot.ResourceMap.AddAsScalar('Ceres-gradient_tolerance', 1e-5)  
>>> algo = ot.Ceres(problem, 'BFGS')  
>>> algo.setStartingPoint([0.0] * 4)  
>>> algo.run()  
>>> result = algo.getResult()  
>>> x_star = result.getOptimalPoint()  
>>> y_star = result.getOptimalValue()  

Methods

GetAlgorithmNames()

Accessor to the list of algorithms provided, by names.

getAlgorithmName()

Accessor to the algorithm name.

getCheckStatus()

Accessor to check status flag.

getClassName()

Accessor to the object's name.

getMaximumAbsoluteError()

Accessor to maximum allowed absolute error.

getMaximumCallsNumber()

Accessor to maximum allowed number of calls.

getMaximumConstraintError()

Accessor to maximum allowed constraint error.

getMaximumIterationNumber()

Accessor to maximum allowed number of iterations.

getMaximumRelativeError()

Accessor to maximum allowed relative error.

getMaximumResidualError()

Accessor to maximum allowed residual error.

getMaximumTimeDuration()

Accessor to the maximum duration.

getName()

Accessor to the object's name.

getProblem()

Accessor to optimization problem.

getResult()

Accessor to optimization result.

getStartingPoint()

Accessor to starting point.

hasName()

Test if the object is named.

run()

Launch the optimization.

setAlgorithmName(algoName)

Accessor to the algorithm name.

setCheckStatus(checkStatus)

Accessor to check status flag.

setMaximumAbsoluteError(maximumAbsoluteError)

Accessor to maximum allowed absolute error.

setMaximumCallsNumber(maximumCallsNumber)

Accessor to maximum allowed number of calls

setMaximumConstraintError(maximumConstraintError)

Accessor to maximum allowed constraint error.

setMaximumIterationNumber(maximumIterationNumber)

Accessor to maximum allowed number of iterations.

setMaximumRelativeError(maximumRelativeError)

Accessor to maximum allowed relative error.

setMaximumResidualError(maximumResidualError)

Accessor to maximum allowed residual error.

setMaximumTimeDuration(maximumTime)

Accessor to the maximum duration.

setName(name)

Accessor to the object's name.

setProblem(problem)

Accessor to optimization problem.

setProgressCallback(*args)

Set up a progress callback.

setResult(result)

Accessor to optimization result.

setStartingPoint(startingPoint)

Accessor to starting point.

setStopCallback(*args)

Set up a stop callback.

getMaximumEvaluationNumber

setMaximumEvaluationNumber

__init__(*args)
static GetAlgorithmNames()

Accessor to the list of algorithms provided, by names.

Returns:
namesDescription

List of algorithm names provided, according to its naming convention.

The trust region methods are not able to solve general optimization problems, in that case a warning is printed and the default line search method is used instead.

Examples

>>> import openturns as ot
>>> print(ot.Ceres.GetAlgorithmNames())
[LEVENBERG_MARQUARDT,DOGLEG,STEEPEST_DESCENT,NONLINEAR_CONJUGATE_GRADIENT,LBFGS,BFGS]
getAlgorithmName()

Accessor to the algorithm name.

Returns:
algoNamestr

The identifier of the algorithm.

getCheckStatus()

Accessor to check status flag.

Returns:
checkStatusbool

Whether to check the termination status. If set to False, run() will not throw an exception if the algorithm does not fully converge and will allow one to still find a feasible candidate.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

The object class name (object.__class__.__name__).

getMaximumAbsoluteError()

Accessor to maximum allowed absolute error.

Returns:
maximumAbsoluteErrorfloat

Maximum allowed absolute error, where the absolute error is defined by \epsilon^a_n=\|\vect{x}_{n+1}-\vect{x}_n\|_{\infty} where \vect{x}_{n+1} and \vect{x}_n are two consecutive approximations of the optimum.

getMaximumCallsNumber()

Accessor to maximum allowed number of calls.

Returns:
maximumEvaluationNumberint

Maximum allowed number of direct objective function calls through the () operator. Does not take into account eventual indirect calls through finite difference gradient calls.

getMaximumConstraintError()

Accessor to maximum allowed constraint error.

Returns:
maximumConstraintErrorfloat

Maximum allowed constraint error, where the constraint error is defined by \gamma_n=\|g(\vect{x}_n)\|_{\infty} where \vect{x}_n is the current approximation of the optimum and g is the function that gathers all the equality and inequality constraints (violated values only)

getMaximumIterationNumber()

Accessor to maximum allowed number of iterations.

Returns:
maximumIterationNumberint

Maximum allowed number of iterations.

getMaximumRelativeError()

Accessor to maximum allowed relative error.

Returns:
maximumRelativeErrorfloat

Maximum allowed relative error, where the relative error is defined by \epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty} if \|\vect{x}_{n+1}\|_{\infty}\neq 0, else \epsilon^r_n=-1.

getMaximumResidualError()

Accessor to maximum allowed residual error.

Returns:
maximumResidualErrorfloat

Maximum allowed residual error, where the residual error is defined by \epsilon^r_n=\frac{\|f(\vect{x}_{n+1})-f(\vect{x}_{n})\|}{\|f(\vect{x}_{n+1})\|} if \|f(\vect{x}_{n+1})\|\neq 0, else \epsilon^r_n=-1.

getMaximumTimeDuration()

Accessor to the maximum duration.

Returns:
maximumTimefloat

Maximum optimization duration in seconds.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getProblem()

Accessor to optimization problem.

Returns:
problemOptimizationProblem

Optimization problem.

getResult()

Accessor to optimization result.

Returns:
resultOptimizationResult

Result class.

getStartingPoint()

Accessor to starting point.

Returns:
startingPointPoint

Starting point.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

run()

Launch the optimization.

setAlgorithmName(algoName)

Accessor to the algorithm name.

Parameters:
algoNamestr

The identifier of the algorithm.

setCheckStatus(checkStatus)

Accessor to check status flag.

Parameters:
checkStatusbool

Whether to check the termination status. If set to False, run() will not throw an exception if the algorithm does not fully converge and will allow one to still find a feasible candidate.

setMaximumAbsoluteError(maximumAbsoluteError)

Accessor to maximum allowed absolute error.

Parameters:
maximumAbsoluteErrorfloat

Maximum allowed absolute error, where the absolute error is defined by \epsilon^a_n=\|\vect{x}_{n+1}-\vect{x}_n\|_{\infty} where \vect{x}_{n+1} and \vect{x}_n are two consecutive approximations of the optimum.

setMaximumCallsNumber(maximumCallsNumber)

Accessor to maximum allowed number of calls

Parameters:
maximumEvaluationNumberint

Maximum allowed number of direct objective function calls through the () operator. Does not take into account eventual indirect calls through finite difference gradient calls.

setMaximumConstraintError(maximumConstraintError)

Accessor to maximum allowed constraint error.

Parameters:
maximumConstraintErrorfloat

Maximum allowed constraint error, where the constraint error is defined by \gamma_n=\|g(\vect{x}_n)\|_{\infty} where \vect{x}_n is the current approximation of the optimum and g is the function that gathers all the equality and inequality constraints (violated values only)

setMaximumIterationNumber(maximumIterationNumber)

Accessor to maximum allowed number of iterations.

Parameters:
maximumIterationNumberint

Maximum allowed number of iterations.

setMaximumRelativeError(maximumRelativeError)

Accessor to maximum allowed relative error.

Parameters:
maximumRelativeErrorfloat

Maximum allowed relative error, where the relative error is defined by \epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty} if \|\vect{x}_{n+1}\|_{\infty}\neq 0, else \epsilon^r_n=-1.

setMaximumResidualError(maximumResidualError)

Accessor to maximum allowed residual error.

Parameters:
maximumResidualErrorfloat

Maximum allowed residual error, where the residual error is defined by \epsilon^r_n=\frac{\|f(\vect{x}_{n+1})-f(\vect{x}_{n})\|}{\|f(\vect{x}_{n+1})\|} if \|f(\vect{x}_{n+1})\|\neq 0, else \epsilon^r_n=-1.

setMaximumTimeDuration(maximumTime)

Accessor to the maximum duration.

Parameters:
maximumTimefloat

Maximum optimization duration in seconds.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setProblem(problem)

Accessor to optimization problem.

Parameters:
problemOptimizationProblem

Optimization problem.

setProgressCallback(*args)

Set up a progress callback.

Can be used to programmatically report the progress of an optimization.

Parameters:
callbackcallable

Takes a float as argument as percentage of progress.

Examples

>>> import sys
>>> import openturns as ot
>>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2'])
>>> problem = ot.OptimizationProblem(rosenbrock)
>>> solver = ot.OptimizationAlgorithm(problem)
>>> solver.setStartingPoint([0, 0])
>>> solver.setMaximumResidualError(1.e-3)
>>> solver.setMaximumCallsNumber(10000)
>>> def report_progress(progress):
...     sys.stderr.write('-- progress=' + str(progress) + '%\n')
>>> solver.setProgressCallback(report_progress)
>>> solver.run()
setResult(result)

Accessor to optimization result.

Parameters:
resultOptimizationResult

Result class.

setStartingPoint(startingPoint)

Accessor to starting point.

Parameters:
startingPointPoint

Starting point.

setStopCallback(*args)

Set up a stop callback.

Can be used to programmatically stop an optimization.

Parameters:
callbackcallable

Returns an int deciding whether to stop or continue.

Examples

>>> import openturns as ot
>>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2'])
>>> problem = ot.OptimizationProblem(rosenbrock)
>>> solver = ot.OptimizationAlgorithm(problem)
>>> solver.setStartingPoint([0, 0])
>>> solver.setMaximumResidualError(1.e-3)
>>> solver.setMaximumCallsNumber(10000)
>>> def ask_stop():
...     return True
>>> solver.setStopCallback(ask_stop)
>>> solver.run()