OptimizationResult¶

class OptimizationResult(*args)¶

Optimization result.

Returned by optimization solvers, see OptimizationAlgorithm.

Available constructors:: OptimizationResult(optimalPoint, optimalValue, evaluationNumber, absoluteError, relativeError, residualError, constraintError)

Parameters

optimalPointsequence of float: Optimal point.
optimalValuesequence of float: Value at optimal point.
evaluationNumberint: Number of evaluations.
absoluteErrorfloat: Parameters for this solver.
relativeErrorfloat: Relative error.
residualErrorfloat: Residual error.
constraintErrorfloat: Constraint error.
problemOptimizationProblem: Associated problem.

Methods

`drawErrorHistory`(self)	Draw the convergence criteria history.
`drawOptimalValueHistory`(self)	Draw the optimal value history.
`getAbsoluteError`(self)	Accessor to the absolute error.
`getAbsoluteErrorHistory`(self)	Accessor to the evolution of the absolute error.
`getClassName`(self)	Accessor to the object’s name.
`getConstraintError`(self)	Accessor to the constraint error.
`getConstraintErrorHistory`(self)	Accessor to the evolution of the constraint error.
`getEvaluationNumber`(self)	Accessor to the number of evaluations.
`getId`(self)	Accessor to the object’s id.
`getInputSample`(self)	Accessor to the input sample.
`getIterationNumber`(self)	Accessor to the number of iterations.
`getLagrangeMultipliers`(self)	Accessor to the Lagrange multipliers.
`getName`(self)	Accessor to the object’s name.
`getOptimalPoint`(self)	Accessor to the optimal point.
`getOptimalValue`(self)	Accessor to the optimal value.
`getOutputSample`(self)	Accessor to the output sample.
`getProblem`(self)	Accessor to the underlying optimization problem.
`getRelativeError`(self)	Accessor to the relative error.
`getRelativeErrorHistory`(self)	Accessor to the evolution of the relative error.
`getResidualError`(self)	Accessor to the residual error.
`getResidualErrorHistory`(self)	Accessor to the evolution of the residual error.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getVisibility`(self)	Accessor to the object’s visibility state.
`hasName`(self)	Test if the object is named.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`setEvaluationNumber`(self, evaluationNumber)	Accessor to the number of evaluation.
`setIterationNumber`(self, iterationNumber)	Accessor to the number of iterations.
`setLagrangeMultipliers`(self, lagrangeMultipliers)	Accessor to the Lagrange multipliers.
`setName`(self, name)	Accessor to the object’s name.
`setOptimalPoint`(self, optimalPoint)	Accessor to the optimal point.
`setOptimalValue`(self, optimalValue)	Accessor to the optimal value.
`setProblem`(self, problem)	Accessor to the underlying optimization problem.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.

store

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

drawErrorHistory(self)¶

Draw the convergence criteria history.

Returns

graphGraph: Convergence criteria history graph

drawOptimalValueHistory(self)¶

Draw the optimal value history.

Returns

graphGraph: Optimal value history graph

getAbsoluteError(self)¶

Accessor to the absolute error.

Returns

absoluteErrorfloat: Absolute error, 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.

getAbsoluteErrorHistory(self)¶

Accessor to the evolution of the absolute error.

Returns

absoluteErrorHistorySample: Value of the absolute error at each function evaluation.

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getConstraintError(self)¶

Accessor to the constraint error.

Returns

constraintErrorfloat: Constraint error, 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).

getConstraintErrorHistory(self)¶

Accessor to the evolution of the constraint error.

Returns

constraintErrorHistorySample: Value of the constraint error at each function evaluation.

getEvaluationNumber(self)¶

Accessor to the number of evaluations.

Returns

evaluationNumberint: Number of evaluations.

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getInputSample(self)¶

Accessor to the input sample.

Returns

inputSampleSample: Input points used by the solver

getIterationNumber(self)¶

Accessor to the number of iterations.

Returns

iterationNumberint: Number of iterations.

getLagrangeMultipliers(self)¶

Accessor to the Lagrange multipliers.

Returns

multipliersPoint: Lagrange multipliers.

Notes

See OptimizationAlgorithm for the details on how the multipliers are defined and stored in the result.

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getOptimalPoint(self)¶

Accessor to the optimal point.

Returns

optimalPointPoint: Optimal point

getOptimalValue(self)¶

Accessor to the optimal value.

Returns

optimalValuePoint: Value at the optimal point

getOutputSample(self)¶

Accessor to the output sample.

Returns

outputSampleSample: Output points used by the solver

getProblem(self)¶

Accessor to the underlying optimization problem.

Returns

problemOptimizationProblem: Problem corresponding to the result

getRelativeError(self)¶

Accessor to the relative error.

Returns

relativeErrorfloat: Relative error, defined by $\epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty}$ if $\|\vect{x}_{n+1}\|_{\infty}\neq 0$ , else $\epsilon^r_n=-1$ .

getRelativeErrorHistory(self)¶

Accessor to the evolution of the relative error.

Returns

relativeErrorHistorySample: Value of the relative error at each function evaluation.

getResidualError(self)¶

Accessor to the residual error.

Returns

residualErrorfloat: Relative error, 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 $\epsilon^r_n=-1$ .

getResidualErrorHistory(self)¶

Accessor to the evolution of the residual error.

Returns

residualErrorHistorySample: Value of the residual error at each function evaluation.

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

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

setEvaluationNumber(self, evaluationNumber)¶

Accessor to the number of evaluation.

Parameters

evaluationNumberint: Number of evaluations.

setIterationNumber(self, iterationNumber)¶

Accessor to the number of iterations.

Parameters

iterationNumberint: Number of iterations.

setLagrangeMultipliers(self, lagrangeMultipliers)¶

Accessor to the Lagrange multipliers.

Parameters

multipliersPoint: Lagrange multipliers.

Notes

See OptimizationAlgorithm for the details on how the multipliers are defined and stored in the result.

setName(self, name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setOptimalPoint(self, optimalPoint)¶

Accessor to the optimal point.

Parameters

optimalPointPoint: Optimal point

setOptimalValue(self, optimalValue)¶

Accessor to the optimal value.

Parameters

optimalValuePoint: Value at the optimal point

setProblem(self, problem)¶

Accessor to the underlying optimization problem.

Parameters

problemOptimizationProblem: Problem corresponding to the result

setShadowedId(self, id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setVisibility(self, 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

Previous topic

Next topic

This Page

OptimizationResult¶