MultiStart¶

class MultiStart(*args)¶

Multi start optimization algorithm.

The algorithm runs an optimization solver for N starting points and returns the best result of each local search. The algorithm succeeds when at least one local search succeeds.

Available constructors:

MultiStart(solver, startingPoints)

Parameters

solverOptimizationAlgorithm: The internal solver
startingPoints2-d sequence of float: Starting point candidates

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> dim = 2
>>> model = ot.SymbolicFunction(['x', 'y'], ['x^2+y^2*(1-x)^3'])
>>> bounds = ot.Interval([-3.0] * dim, [3.0] * dim)
>>> problem = ot.OptimizationProblem(model)
>>> problem.setBounds(bounds)
>>> solver = ot.TNC(problem)
>>> startingPoints = ot.Normal(dim).getSample(3)
>>> algo = ot.MultiStart(solver, startingPoints)
>>> algo.run()
>>> result = algo.getResult()

Methods

`getClassName`()	Accessor to the object’s name.
`getId`()	Accessor to the object’s id.
`getKeepResults`()	Flag to keep intermediate results accessor.
`getMaximumAbsoluteError`()	Accessor to maximum allowed absolute error.
`getMaximumConstraintError`()	Accessor to maximum allowed constraint error.
`getMaximumEvaluationNumber`()	Accessor to maximum allowed number of evaluations.
`getMaximumIterationNumber`()	Accessor to maximum allowed number of iterations.
`getMaximumRelativeError`()	Accessor to maximum allowed relative error.
`getMaximumResidualError`()	Accessor to maximum allowed residual error.
`getName`()	Accessor to the object’s name.
`getOptimizationAlgorithm`()	Solver accessor.
`getProblem`()	Accessor to optimization problem.
`getResult`()	Accessor to optimization result.
`getResultCollection`()	Intermediate optimization results accessor.
`getShadowedId`()	Accessor to the object’s shadowed id.
`getStartingPoint`()	Accessor to starting point.
`getStartingPoints`()	Starting points accessor.
`getVerbose`()	Accessor to the verbosity flag.
`getVisibility`()	Accessor to the object’s visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`run`()	Launch the optimization.
`setKeepResults`(keepResults)	Flag to keep intermediate results accessor.
`setMaximumAbsoluteError`(maximumAbsoluteError)	Accessor to maximum allowed absolute error.
`setMaximumConstraintError`(maximumConstraintError)	Accessor to maximum allowed constraint error.
`setMaximumEvaluationNumber`(…)	Accessor to maximum allowed number of evaluations.
`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.
`setName`(name)	Accessor to the object’s name.
`setOptimizationAlgorithm`(solver)	Solver accessor.
`setProblem`(problem)	Accessor to optimization problem.
`setProgressCallback`(*args)	Set up a progress callback.
`setResult`(result)	Accessor to optimization result.
`setShadowedId`(id)	Accessor to the object’s shadowed id.
`setStartingPoint`(startingPoint)	Accessor to starting point.
`setStartingPoints`(sample)	Starting points accessor.
`setStopCallback`(*args)	Set up a stop callback.
`setVerbose`(verbose)	Accessor to the verbosity flag.
`setVisibility`(visible)	Accessor to the object’s visibility state.

computeLagrangeMultipliers

__init__(*args)¶: Initialize self. See help(type(self)) for accurate signature.

getClassName()¶

Accessor to the object’s name.

Returns

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

getId()¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getKeepResults()¶

Flag to keep intermediate results accessor.

Returns

keepResultsbool: If True all the intermediate results are stored, otherwise they are ignored. Default value is MultiStart-KeepResults in ResourceMap

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.

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)

getMaximumEvaluationNumber()¶

Accessor to maximum allowed number of evaluations.

Returns

Nint: Maximum allowed number of evaluations.

getMaximumIterationNumber()¶

Accessor to maximum allowed number of iterations.

Returns

Nint: 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 $\epsilon^r_n=-1$ .

getName()¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getOptimizationAlgorithm()¶

Solver accessor.

Returns

solverOptimizationAlgorithm: The internal solver

getProblem()¶

Accessor to optimization problem.

Returns

problemOptimizationProblem: Optimization problem.

getResult()¶

Accessor to optimization result.

Returns

resultOptimizationResult: Result class.

getResultCollection()¶

Intermediate optimization results accessor.

Returns

resultsOptimizationResultCollection: Intermediate optimization results

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getStartingPoint()¶

Accessor to starting point.

Returns

startingPointPoint: Starting point.

getStartingPoints()¶

Starting points accessor.

Returns

startingPointNumberSample: Starting points

getVerbose()¶

Accessor to the verbosity flag.

Returns

verbosebool: Verbosity flag state.

getVisibility()¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool: True if the name is not empty and not the default one.

run()¶: Launch the optimization.

setKeepResults(keepResults)¶

Flag to keep intermediate results accessor.

Parameters

keepResultsbool: If True all the intermediate results are stored, otherwise they are ignored. Default value is MultiStart-KeepResults in ResourceMap

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.

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)

setMaximumEvaluationNumber(maximumEvaluationNumber)¶

Accessor to maximum allowed number of evaluations.

Parameters

Nint: Maximum allowed number of evaluations.

setMaximumIterationNumber(maximumIterationNumber)¶

Accessor to maximum allowed number of iterations.

Parameters

Nint: 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

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(name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setOptimizationAlgorithm(solver)¶

Solver accessor.

Parameters

solverOptimizationAlgorithm: The internal solver

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.setMaximumEvaluationNumber(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.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setStartingPoint(startingPoint)¶

Accessor to starting point.

Parameters

startingPointPoint: Starting point.

setStartingPoints(sample)¶

Starting points accessor.

Parameters

startingPointNumberSample: Starting points

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.setMaximumEvaluationNumber(10000)
>>> def ask_stop():
...     return True
>>> solver.setStopCallback(ask_stop)
>>> solver.run()

setVerbose(verbose)¶

Accessor to the verbosity flag.

Parameters

verbosebool: Verbosity flag state.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

OpenTURNS

An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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