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:
solver : OptimizationAlgorithm

The internal solver

startingPoints : 2-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

computeLagrangeMultipliers(x) Compute the Lagrange multipliers of a problem at a given point.
getClassName() Accessor to the object’s name.
getId() Accessor to the object’s id.
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.
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.
__init__(*args)

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

computeLagrangeMultipliers(x)

Compute the Lagrange multipliers of a problem at a given point.

Parameters:
x : sequence of float

Point at which the Lagrange multipliers are computed.
Returns:
lagrangeMultiplier : sequence of float

Lagrange multipliers of the problem at the given point.

Notes

The Lagrange multipliers \vect{\lambda} are associated with the following Lagrangian formulation of the optimization problem:

\cL(\vect{x}, \vect{\lambda}_{eq}, \vect{\lambda}_{\ell}, \vect{\lambda}_{u}, \vect{\lambda}_{ineq}) = J(\vect{x}) + \Tr{\vect{\lambda}}_{eq} g(\vect{x}) + \Tr{\vect{\lambda}}_{\ell} (\vect{x}-\vect{\ell})^{+} + \Tr{\vect{\lambda}}_{u} (\vect{u}-\vect{x})^{+} + \Tr{\vect{\lambda}}_{ineq} h^{+}(\vect{x})

where \vect{\alpha}^{+}=(\max(0,\alpha_1),\hdots,\max(0,\alpha_n)).

The Lagrange multipliers are stored as (\vect{\lambda}_{eq}, \vect{\lambda}_{\ell}, \vect{\lambda}_{u}, \vect{\lambda}_{ineq}), where:
  • \vect{\lambda}_{eq} is of dimension 0 if there is no equality constraint, else of dimension the dimension of g(\vect{x}) ie the number of scalar equality constraints
  • \vect{\lambda}_{\ell} and \vect{\lambda}_{u} are of dimension 0 if there is no bound constraint, else of dimension of \vect{x}
  • \vect{\lambda}_{eq} is of dimension 0 if there is no inequality constraint, else of dimension the dimension of h(\vect{x}) ie the number of scalar inequality constraints

The vector \vect{\lambda} is solution of the following linear system:

\Tr{\vect{\lambda}}_{eq}\left[\dfrac{\partial g}{\partial\vect{x}}(\vect{x})\right]+ \Tr{\vect{\lambda}}_{\ell}\left[\dfrac{\partial (\vect{x}-\vect{\ell})^{+}}{\partial\vect{x}}(\vect{x})\right]+ \Tr{\vect{\lambda}}_{u}\left[\dfrac{\partial (\vect{u}-\vect{x})^{+}}{\partial\vect{x}}(\vect{x})\right]+ \Tr{\vect{\lambda}}_{ineq}\left[\dfrac{\partial h}{\partial\vect{x}}(\vect{x})\right]=-\dfrac{\partial J}{\partial\vect{x}}(\vect{x})

If there is no constraint of any kind, \vect{\lambda} is of dimension 0, as well as if no constraint is active.

getClassName()

Accessor to the object’s name.

Returns:
class_name : str

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

Accessor to the object’s id.

Returns:
id : int

Internal unique identifier.
getMaximumAbsoluteError()

Accessor to maximum allowed absolute error.

Returns:
maximumAbsoluteError : float

Maximum allowed absolute error.
getMaximumConstraintError()

Accessor to maximum allowed constraint error.

Returns:
maximumConstraintError : float

Maximum allowed constraint error.
getMaximumEvaluationNumber()

Accessor to maximum allowed number of evaluations.

Returns:
N : int

Maximum allowed number of evaluations.
getMaximumIterationNumber()

Accessor to maximum allowed number of iterations.

Returns:
N : int

Maximum allowed number of iterations.
getMaximumRelativeError()

Accessor to maximum allowed relative error.

Returns:
maximumRelativeError : float

Maximum allowed relative error.
getMaximumResidualError()

Accessor to maximum allowed residual error.

Returns:
maximumResidualError : float

Maximum allowed residual error.
getName()

Accessor to the object’s name.

Returns:
name : str

The name of the object.
getOptimizationAlgorithm()

Solver accessor.

Returns:
solver : OptimizationAlgorithm

The internal solver
getProblem()

Accessor to optimization problem.

Returns:
problem : OptimizationProblem

Optimization problem.
getResult()

Accessor to optimization result.

Returns:
result : OptimizationResult

Result class.
getResultCollection()

Intermediate optimization results accessor.

Returns:
results : OptimizationResultCollection

Intermediate optimization results
getShadowedId()

Accessor to the object’s shadowed id.

Returns:
id : int

Internal unique identifier.
getStartingPoint()

Accessor to starting point.

Returns:
startingPoint : Point

Starting point.
getStartingPoints()

Starting points accessor.

Returns:
startingPointNumber : Sample

Starting points
getVerbose()

Accessor to the verbosity flag.

Returns:
verbose : bool

Verbosity flag state.
getVisibility()

Accessor to the object’s visibility state.

Returns:
visible : bool

Visibility flag.
hasName()

Test if the object is named.

Returns:
hasName : bool

True if the name is not empty.
hasVisibleName()

Test if the object has a distinguishable name.

Returns:
hasVisibleName : bool

True if the name is not empty and not the default one.
run()

Launch the optimization.

setMaximumAbsoluteError(maximumAbsoluteError)

Accessor to maximum allowed absolute error.

Parameters:
maximumAbsoluteError : float

Maximum allowed absolute error.
setMaximumConstraintError(maximumConstraintError)

Accessor to maximum allowed constraint error.

Parameters:
maximumConstraintError : float

Maximum allowed constraint error.
setMaximumEvaluationNumber(maximumEvaluationNumber)

Accessor to maximum allowed number of evaluations.

Parameters:
N : int

Maximum allowed number of evaluations.
setMaximumIterationNumber(maximumIterationNumber)

Accessor to maximum allowed number of iterations.

Parameters:
N : int

Maximum allowed number of iterations.
setMaximumRelativeError(maximumRelativeError)

Accessor to maximum allowed relative error.

Parameters:
maximumRelativeError : float

Maximum allowed relative error.
setMaximumResidualError(maximumResidualError)

Accessor to maximum allowed residual error.

Parameters:
maximumResidualError : float

Maximum allowed residual error.
setName(name)

Accessor to the object’s name.

Parameters:
name : str

The name of the object.
setOptimizationAlgorithm(solver)

Solver accessor.

Parameters:
solver : OptimizationAlgorithm

The internal solver
setProblem(problem)

Accessor to optimization problem.

Parameters:
problem : OptimizationProblem

Optimization problem.
setProgressCallback(*args)

Set up a progress callback.

Parameters:
callback : callable

Takes a float as argument as percentage of progress.

Notes

May not be implemented by all solvers, refer to the solver documentation.

setResult(result)

Accessor to optimization result.

Parameters:
result : OptimizationResult

Result class.
setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters:
id : int

Internal unique identifier.
setStartingPoint(startingPoint)

Accessor to starting point.

Parameters:
startingPoint : Point

Starting point.
setStartingPoints(sample)

Starting points accessor.

Parameters:
startingPointNumber : Sample

Starting points
setStopCallback(*args)

Set up a stop callback.

Parameters:
callback : callable

Returns an int deciding whether to stop or continue.

Notes

May not be implemented by all solvers, refer to the solver documentation.

setVerbose(verbose)

Accessor to the verbosity flag.

Parameters:
verbose : bool

Verbosity flag state.
setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visible : bool

Visibility flag.