fr.cnes.sirius.patrius.math.optim.joptimizer.functions

Interface BarrierFunction

All Superinterfaces:

TwiceDifferentiableMultivariateRealFunction

All Known Implementing Classes:

LogarithmicBarrier

public interface BarrierFunction
extends TwiceDifferentiableMultivariateRealFunction

Interface for the barrier function used by a given barrier optimization method.

Since:

4.6

Author:

alberto trivellato (alberto.trivellato@gmail.com) HISTORY VERSION:4.6:DM:DM-2591:27/01/2021:[PATRIUS] Intigration et validation JOptimizer END-HISTORY

See Also:

"S.Boyd and L.Vandenberghe, Convex Optimization, 11.2"

Method Summary

Modifier and Type	Method and Description
double	calculatePhase1InitialFeasiblePoint(double[] originalNotFeasiblePoint, double tolerance) Calculates the initial value for the additional variable s in basic Phase I method.
BarrierFunction	createPhase1BarrierFunction() Create the barrier function for the basic Phase I method.
double	getDualityGap(double t) Calculates the duality gap for a barrier method build with this barrier function.

Methods inherited from interface fr.cnes.sirius.patrius.math.optim.joptimizer.functions.TwiceDifferentiableMultivariateRealFunction

getDim, gradient, hessian, value

Method Detail

getDualityGap

double getDualityGap(double t)

Calculates the duality gap for a barrier method build with this barrier function.

Parameters:

t - value

Returns:

duality gap

createPhase1BarrierFunction

BarrierFunction createPhase1BarrierFunction()

Create the barrier function for the basic Phase I method.

Returns:

phase 1 barrier function

See Also:

"S.Boyd and L.Vandenberghe, Convex Optimization, 11.4.1"

calculatePhase1InitialFeasiblePoint

double calculatePhase1InitialFeasiblePoint(double[] originalNotFeasiblePoint,
                                           double tolerance)

Calculates the initial value for the additional variable s in basic Phase I method.

Parameters:

originalNotFeasiblePoint - initial point (not-feasible)

tolerance - tolerance

Returns:

phase 1 initial feasible point

See Also:

"S.Boyd and L.Vandenberghe, Convex Optimization, 11.4.1"