Class SQPASEVariation2
- java.lang.Object
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- dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
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- dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
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- All Implemented Interfaces:
SQPASEVariation
public class SQPASEVariation2 extends SQPASEVariation1
This implementation tries to find an exact positive definite Hessian whenever possible.
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Field Summary
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Fields inherited from class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
a, discretization, epsilon, f, foundPositiveDefiniteHessian, lower, p, r
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Constructor Summary
Constructors Constructor Description SQPASEVariation2()
Construct a variation.SQPASEVariation2(double r, double lower, int discretization)
Construct a variation.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Matrix
updateHessian(Vector x1, Vector v1, Vector d, Vector g0, Matrix A0, Matrix W0)
Update the Hessian matrix using the latest iterates.-
Methods inherited from class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
alpha, getInitialHessian, set, W
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Constructor Detail
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SQPASEVariation2
public SQPASEVariation2(double r, double lower, int discretization)
Construct a variation.- Parameters:
r
- Han's exact penalty function coefficient, the bigger the better, e.g., eq. 15.20lower
- the lower bound of alpha; the smaller the better but cannot be zerodiscretization
- the number of points between [lower, 1] to search for alpha; the bigger the better
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SQPASEVariation2
public SQPASEVariation2()
Construct a variation.
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Method Detail
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updateHessian
public Matrix updateHessian(Vector x1, Vector v1, Vector d, Vector g0, Matrix A0, Matrix W0)
Description copied from interface:SQPASEVariation
Update the Hessian matrix using the latest iterates.- Specified by:
updateHessian
in interfaceSQPASEVariation
- Overrides:
updateHessian
in classSQPASEVariation1
- Parameters:
x1
- the next minimizerv1
- the next Lagrange multipliers for inequality constraints (mu)d
- the minimizer incrementg0
- the gradientA0
- the set of active equality constraintsW0
- the current Hessian matrix- Returns:
- the next Hessian matrix
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