OptimizationAlgorithm

class OptimizationAlgorithm(*args)

Base class for optimization wrappers.

Available constructors:

OptimizationAlgorithm(problem, verbose=False)

Parameters
problemOptimizationProblem

Optimization problem.

verbosebool

Let solver be more verbose.

Notes

Class OptimizationAlgorithm is an abstract class, which has several implementations. The default implementation is Cobyla

Examples

Define an optimization problem to find the minimum of the Rosenbrock function:

>>> 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.setMaximumIterationNumber(100)
>>> solver.run()
>>> result = solver.getResult()
>>> x_star = result.getOptimalPoint()
>>> y_star = result.getOptimalValue()

Methods

Build(\*args)

Instanciate an optimization algorithm from name or problem.

GetAlgorithmNames(\*args)

Get the list of available solver names.

getClassName(self)

Accessor to the object’s name.

getId(self)

Accessor to the object’s id.

getImplementation(self, \*args)

Accessor to the underlying implementation.

getMaximumAbsoluteError(self)

Accessor to maximum allowed absolute error.

getMaximumConstraintError(self)

Accessor to maximum allowed constraint error.

getMaximumEvaluationNumber(self)

Accessor to maximum allowed number of evaluations.

getMaximumIterationNumber(self)

Accessor to maximum allowed number of iterations.

getMaximumRelativeError(self)

Accessor to maximum allowed relative error.

getMaximumResidualError(self)

Accessor to maximum allowed residual error.

getName(self)

Accessor to the object’s name.

getProblem(self)

Accessor to optimization problem.

getResult(self)

Accessor to optimization result.

getStartingPoint(self)

Accessor to starting point.

getVerbose(self)

Accessor to the verbosity flag.

run(self)

Launch the optimization.

setMaximumAbsoluteError(self, …)

Accessor to maximum allowed absolute error.

setMaximumConstraintError(self, …)

Accessor to maximum allowed constraint error.

setMaximumEvaluationNumber(self, …)

Accessor to maximum allowed number of evaluations.

setMaximumIterationNumber(self, …)

Accessor to maximum allowed number of iterations.

setMaximumRelativeError(self, …)

Accessor to maximum allowed relative error.

setMaximumResidualError(self, …)

Accessor to maximum allowed residual error.

setName(self, name)

Accessor to the object’s name.

setProblem(self, problem)

Accessor to optimization problem.

setProgressCallback(self, \*args)

Set up a progress callback.

setResult(self, result)

Accessor to optimization result.

setStartingPoint(self, startingPoint)

Accessor to starting point.

setStopCallback(self, \*args)

Set up a stop callback.

setVerbose(self, verbose)

Accessor to the verbosity flag.

GetLeastSquaresAlgorithmNames

__init__(self, *args)

Initialize self. See help(type(self)) for accurate signature.

static Build(*args)

Instanciate an optimization algorithm from name or problem.

Parameters
namestr or OptimizationProblem

Name of the algorithm or problem to solve. For example TNC, Cobyla or one of the NLopt solver names.

static GetAlgorithmNames(*args)

Get the list of available solver names.

Parameters
problemOptimizationProblem, optional

Problem to solve.

Returns
namesDescription

List of available solver names.

getClassName(self)

Accessor to the object’s name.

Returns
class_namestr

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

getId(self)

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getImplementation(self, *args)

Accessor to the underlying implementation.

Returns
implImplementation

The implementation class.

getMaximumAbsoluteError(self)

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.

getMaximumConstraintError(self)

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)

getMaximumEvaluationNumber(self)

Accessor to maximum allowed number of evaluations.

Returns
Nint

Maximum allowed number of evaluations.

getMaximumIterationNumber(self)

Accessor to maximum allowed number of iterations.

Returns
Nint

Maximum allowed number of iterations.

getMaximumRelativeError(self)

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(self)

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.

getName(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getProblem(self)

Accessor to optimization problem.

Returns
problemOptimizationProblem

Optimization problem.

getResult(self)

Accessor to optimization result.

Returns
resultOptimizationResult

Result class.

getStartingPoint(self)

Accessor to starting point.

Returns
startingPointPoint

Starting point.

getVerbose(self)

Accessor to the verbosity flag.

Returns
verbosebool

Verbosity flag state.

run(self)

Launch the optimization.

setMaximumAbsoluteError(self, 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.

setMaximumConstraintError(self, 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)

setMaximumEvaluationNumber(self, maximumEvaluationNumber)

Accessor to maximum allowed number of evaluations.

Parameters
Nint

Maximum allowed number of evaluations.

setMaximumIterationNumber(self, maximumIterationNumber)

Accessor to maximum allowed number of iterations.

Parameters
Nint

Maximum allowed number of iterations.

setMaximumRelativeError(self, 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(self, maximumResidualError)

Accessor to maximum allowed residual error.

Parameters
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.

setName(self, name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setProblem(self, problem)

Accessor to optimization problem.

Parameters
problemOptimizationProblem

Optimization problem.

setProgressCallback(self, *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.setMaximumIterationNumber(100)
>>> def report_progress(progress):
...     sys.stderr.write('-- progress=' + str(progress) + '%\n')
>>> solver.setProgressCallback(report_progress)
>>> solver.run()
setResult(self, result)

Accessor to optimization result.

Parameters
resultOptimizationResult

Result class.

setStartingPoint(self, startingPoint)

Accessor to starting point.

Parameters
startingPointPoint

Starting point.

setStopCallback(self, *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.setMaximumIterationNumber(100)
>>> def ask_stop():
...     return True
>>> solver.setStopCallback(ask_stop)
>>> solver.run()
setVerbose(self, verbose)

Accessor to the verbosity flag.

Parameters
verbosebool

Verbosity flag state.