OptimizationAlgorithm

class OptimizationAlgorithm(*args)

Base class for optimization wrappers.

Available constructors:
OptimizationAlgorithm(problem, verbose=False)
Parameters:
problem : OptimizationProblem

Optimization problem.

verbose : bool

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()
Attributes:
thisown

The membership flag

Methods

Build(solverName) Instanciate an optimization algorithm from its name.
GetAlgorithmNames() Get the list of available solver names.
getClassName() Accessor to the object’s name.
getId() Accessor to the object’s id.
getImplementation(*args) Accessor to the underlying implementation.
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.
getProblem() Accessor to optimization problem.
getResult() Accessor to optimization result.
getStartingPoint() Accessor to starting point.
getVerbose() Accessor to the verbosity flag.
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.
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.
setVerbose(verbose) Accessor to the verbosity flag.
__init__(*args)

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

static Build(solverName)

Instanciate an optimization algorithm from its name.

Parameters:
name : str

Name of the algorithm. Use TNC, Cobyla or one of the NLopt solver names.

static GetAlgorithmNames()

Get the list of available solver names.

Returns:
names : Description

List of available solver names.

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.

getImplementation(*args)

Accessor to the underlying implementation.

Returns:
impl : Implementation

The implementation class.

getMaximumAbsoluteError()

Accessor to maximum allowed absolute error.

Returns:
maximumAbsoluteError : float

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:
maximumConstraintError : float

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:
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, 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:
maximumResidualError : float

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:
name : str

The name of the object.

getProblem()

Accessor to optimization problem.

Returns:
problem : OptimizationProblem

Optimization problem.

getResult()

Accessor to optimization result.

Returns:
result : OptimizationResult

Result class.

getStartingPoint()

Accessor to starting point.

Returns:
startingPoint : Point

Starting point.

getVerbose()

Accessor to the verbosity flag.

Returns:
verbose : bool

Verbosity flag state.

run()

Launch the optimization.

setMaximumAbsoluteError(maximumAbsoluteError)

Accessor to maximum allowed absolute error.

Parameters:
maximumAbsoluteError : float

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:
maximumConstraintError : float

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:
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, 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:
name : str

The name of the object.

setProblem(problem)

Accessor to optimization problem.

Parameters:
problem : OptimizationProblem

Optimization problem.

setProgressCallback(*args)

Set up a progress callback.

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

Parameters:
callback : callable

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(result)

Accessor to optimization result.

Parameters:
result : OptimizationResult

Result class.

setStartingPoint(startingPoint)

Accessor to starting point.

Parameters:
startingPoint : Point

Starting point.

setStopCallback(*args)

Set up a stop callback.

Can be used to programmatically stop an optimization.

Parameters:
callback : callable

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(verbose)

Accessor to the verbosity flag.

Parameters:
verbose : bool

Verbosity flag state.

thisown

The membership flag