Package org.hipparchus.optim.nonlinear.vector.constrained

Class AbstractSQPOptimizer

java.lang.Object

org.hipparchus.optim.BaseOptimizer<P>

org.hipparchus.optim.BaseMultivariateOptimizer<LagrangeSolution>

org.hipparchus.optim.nonlinear.vector.constrained.ConstraintOptimizer

org.hipparchus.optim.nonlinear.vector.constrained.AbstractSQPOptimizer

Direct Known Subclasses:

SQPOptimizerGM, SQPOptimizerS

public abstract class AbstractSQPOptimizer
extends ConstraintOptimizer

Abstract class for Sequential Quadratic Programming solvers

Since:

3.1

Field Summary

Fields inherited from class org.hipparchus.optim.BaseOptimizer

evaluations, iterations

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	AbstractSQPOptimizer()	Simple constructor.

Method Summary

Modifier and Type	Method	Description
EqualityConstraint	getEqConstraint()	Getter for equality constraint.
InequalityConstraint	getIqConstraint()	Getter for inequality constraint.
TwiceDifferentiableFunction	getObj()	Getter for objective function.
SQPOption	getSettings()	Getter for settings.
protected RealVector	lagrangianGradX(RealVector currentGrad, RealMatrix jacobConstraint, RealVector x, RealVector y)	Compute Lagrangian gradient for variable X
LagrangeSolution	optimize(OptimizationData... optData)	Stores data and performs the optimization.
protected void	parseOptimizationData(OptimizationData... optData)	Scans the list of (required and optional) optimization data that characterize the problem.

Methods inherited from class org.hipparchus.optim.BaseMultivariateOptimizer

getLowerBound, getStartPoint, getUpperBound

Methods inherited from class org.hipparchus.optim.BaseOptimizer

doOptimize, getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount, optimize

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AbstractSQPOptimizer

protected AbstractSQPOptimizer()

Simple constructor.

Method Detail

getSettings

public SQPOption getSettings()

Getter for settings.

Returns:

settings

getObj

public TwiceDifferentiableFunction getObj()

Getter for objective function.

Returns:

objective function

getEqConstraint

public EqualityConstraint getEqConstraint()

Getter for equality constraint.

Returns:

equality constraint

getIqConstraint

public InequalityConstraint getIqConstraint()

Getter for inequality constraint.

Returns:

inequality constraint

optimize

public LagrangeSolution optimize(OptimizationData... optData)

Description copied from class: ConstraintOptimizer

Stores data and performs the optimization.

The list of parameters is open-ended so that sub-classes can extend it with arguments specific to their concrete implementations.

When the method is called multiple times, instance data is overwritten only when actually present in the list of arguments: when not specified, data set in a previous call is retained (and thus is optional in subsequent calls).

Important note: Subclasses must override BaseOptimizer.parseOptimizationData(OptimizationData[]) if they need to register their own options; but then, they must also call super.parseOptimizationData(optData) within that method.

Overrides:

optimize in class ConstraintOptimizer

Parameters:

optData - Optimization data. In addition to those documented in BaseOptimizer, this method will register the following data:

• InitialGuess
• SimpleBounds

Returns:

a point/value pair that satisfies the convergence criteria.

parseOptimizationData

protected void parseOptimizationData(OptimizationData... optData)

Description copied from class: BaseMultivariateOptimizer

Scans the list of (required and optional) optimization data that characterize the problem.

Overrides:

parseOptimizationData in class BaseMultivariateOptimizer<LagrangeSolution>

Parameters:

optData - Optimization data. The following data will be looked for:

• InitialGuess
• SimpleBounds

lagrangianGradX

protected RealVector lagrangianGradX(RealVector currentGrad,
                                     RealMatrix jacobConstraint,
                                     RealVector x,
                                     RealVector y)

Compute Lagrangian gradient for variable X

Parameters:

currentGrad - current gradient

jacobConstraint - Jacobian

x - value of x

y - value of y

Returns:

Lagrangian