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Fix Newton's minimum projection algorithm onto boundaries in case of conditional optimization. Add possibility to detect several optimal points at initialization of the math_GlobOptMin.
271 lines
8.3 KiB
C++
271 lines
8.3 KiB
C++
// Created on: 1996-05-03
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// Created by: Philippe MANGIN
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// Copyright (c) 1996-1999 Matra Datavision
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// Copyright (c) 1999-2014 OPEN CASCADE SAS
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//
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// This file is part of Open CASCADE Technology software library.
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//
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// This library is free software; you can redistribute it and/or modify it under
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// the terms of the GNU Lesser General Public License version 2.1 as published
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// by the Free Software Foundation, with special exception defined in the file
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// OCCT_LGPL_EXCEPTION.txt. Consult the file LICENSE_LGPL_21.txt included in OCCT
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// distribution for complete text of the license and disclaimer of any warranty.
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//
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// Alternatively, this file may be used under the terms of Open CASCADE
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// commercial license or contractual agreement.
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//#ifndef OCCT_DEBUG
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#define No_Standard_RangeError
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#define No_Standard_OutOfRange
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#define No_Standard_DimensionError
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//#endif
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#include <math_Gauss.hxx>
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#include <math_Jacobi.hxx>
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#include <math_MultipleVarFunctionWithHessian.hxx>
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#include <math_NewtonMinimum.hxx>
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#include <Precision.hxx>
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#include <Standard_DimensionError.hxx>
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#include <StdFail_NotDone.hxx>
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//=======================================================================
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//function : math_NewtonMinimum
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//purpose : Constructor
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//=======================================================================
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math_NewtonMinimum::math_NewtonMinimum(
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const math_MultipleVarFunctionWithHessian& theFunction,
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const Standard_Real theTolerance,
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const Standard_Integer theNbIterations,
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const Standard_Real theConvexity,
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const Standard_Boolean theWithSingularity
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)
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: TheStatus (math_NotBracketed),
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TheLocation(1, theFunction.NbVariables()),
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TheGradient(1, theFunction.NbVariables()),
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TheStep (1, theFunction.NbVariables(), 10.0 * theTolerance),
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TheHessian (1, theFunction.NbVariables(), 1, theFunction.NbVariables()),
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PreviousMinimum (0.0),
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TheMinimum (0.0),
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MinEigenValue (0.0),
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XTol (theTolerance),
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CTol (theConvexity),
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nbiter (0),
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NoConvexTreatement(theWithSingularity),
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Convex (Standard_True),
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myIsBoundsDefined(Standard_False),
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myLeft(1, theFunction.NbVariables(), 0.0),
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myRight(1, theFunction.NbVariables(), 0.0),
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Done (Standard_False),
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Itermax (theNbIterations)
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{
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}
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//=======================================================================
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//function : ~math_NewtonMinimum
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//purpose : Destructor
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//=======================================================================
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math_NewtonMinimum::~math_NewtonMinimum()
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{
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}
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//=======================================================================
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//function : SetBoundary
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//purpose : Set boundaries for conditional optimization
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//=======================================================================
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void math_NewtonMinimum::SetBoundary(const math_Vector& theLeftBorder,
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const math_Vector& theRightBorder)
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{
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myLeft = theLeftBorder;
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myRight = theRightBorder;
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myIsBoundsDefined = Standard_True;
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}
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//=======================================================================
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//function : Perform
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//purpose :
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//=======================================================================
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void math_NewtonMinimum::Perform(math_MultipleVarFunctionWithHessian& F,
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const math_Vector& StartingPoint)
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{
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math_Vector Point1 (1, F.NbVariables());
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Point1 = StartingPoint;
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math_Vector Point2(1, F.NbVariables());
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math_Vector* precedent = &Point1;
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math_Vector* suivant = &Point2;
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math_Vector* auxiliaire = precedent;
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Standard_Boolean Ok = Standard_True;
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Standard_Integer NbConv = 0, ii, Nreduction;
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Standard_Real VPrecedent, VItere;
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Done = Standard_True;
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TheStatus = math_OK;
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nbiter = 0;
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while ( Ok && (NbConv < 2) ) {
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nbiter++;
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// Positionnement
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Ok = F.Values(*precedent, VPrecedent, TheGradient, TheHessian);
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if (!Ok) {
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Done = Standard_False;
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TheStatus = math_FunctionError;
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return;
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}
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if (nbiter==1) {
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PreviousMinimum = VPrecedent;
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TheMinimum = VPrecedent;
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}
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// Traitement de la non convexite
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math_Jacobi CalculVP(TheHessian);
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if ( !CalculVP.IsDone() ) {
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Done = Standard_False;
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TheStatus = math_FunctionError;
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return;
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}
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MinEigenValue = CalculVP.Values() ( CalculVP.Values().Min());
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if ( MinEigenValue < CTol)
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{
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Convex = Standard_False;
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if (NoConvexTreatement && // Treatment is allowed.
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Abs (MinEigenValue) > CTol) // Treatment will have effect.
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{
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Standard_Real Delta = CTol + 0.1 * Abs(MinEigenValue) - MinEigenValue;
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for (ii=1; ii<=TheGradient.Length(); ii++)
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TheHessian(ii, ii) += Delta;
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}
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else
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{
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Done = Standard_False;
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TheStatus = math_FunctionError;
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return;
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}
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}
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// Schemas de Newton
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math_Gauss LU(TheHessian, CTol/100);
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if ( !LU.IsDone()) {
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Done = Standard_False;
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TheStatus = math_DirectionSearchError;
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return;
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}
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LU.Solve(TheGradient, TheStep);
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if (myIsBoundsDefined)
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{
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// Project point on bounds or nullify TheStep coords if point lies on boudary.
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*suivant = *precedent - TheStep;
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Standard_Real aMult = RealLast();
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for(Standard_Integer anIdx = 1; anIdx <= myLeft.Upper(); anIdx++)
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{
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const Standard_Real anAbsStep = Abs(TheStep(anIdx));
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if (anAbsStep < gp::Resolution())
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continue;
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if (suivant->Value(anIdx) < myLeft(anIdx))
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{
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Standard_Real aValue = Abs(precedent->Value(anIdx) - myLeft(anIdx)) / anAbsStep;
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aMult = Min (aValue, aMult);
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}
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if (suivant->Value(anIdx) > myRight(anIdx))
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{
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Standard_Real aValue = Abs(precedent->Value(anIdx) - myRight(anIdx)) / anAbsStep;
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aMult = Min (aValue, aMult);
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}
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}
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if (aMult != RealLast())
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{
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if (aMult > Precision::PConfusion())
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{
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// Project point into param space.
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TheStep *= aMult;
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}
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else
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{
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// Old point on border and new point out of border:
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// Nullify corresponding TheStep indexes.
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for(Standard_Integer anIdx = 1; anIdx <= myLeft.Upper(); anIdx++)
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{
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if ((Abs(precedent->Value(anIdx) - myRight(anIdx)) < Precision::PConfusion() && TheStep(anIdx) < 0.0) ||
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(Abs(precedent->Value(anIdx) - myLeft(anIdx) ) < Precision::PConfusion() && TheStep(anIdx) > 0.0) )
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{
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TheStep(anIdx) = 0.0;
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}
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}
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}
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}
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}
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Standard_Boolean hasProblem = Standard_False;
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do
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{
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*suivant = *precedent - TheStep;
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// Gestion de la convergence
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hasProblem = !(F.Value(*suivant, TheMinimum));
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if (hasProblem)
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{
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TheStep /= 2.0;
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}
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} while (hasProblem);
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if (IsConverged()) { NbConv++; }
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else { NbConv=0; }
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// Controle et corrections.
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VItere = TheMinimum;
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TheMinimum = PreviousMinimum;
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Nreduction =0;
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while (VItere > VPrecedent && Nreduction < 10) {
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TheStep *= 0.4;
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*suivant = *precedent - TheStep;
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F.Value(*suivant, VItere);
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Nreduction++;
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}
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if (VItere <= VPrecedent) {
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auxiliaire = precedent;
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precedent = suivant;
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suivant = auxiliaire;
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PreviousMinimum = VPrecedent;
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TheMinimum = VItere;
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Ok = (nbiter < Itermax);
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if (!Ok && NbConv < 2) TheStatus = math_TooManyIterations;
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}
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else {
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Ok = Standard_False;
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TheStatus = math_DirectionSearchError;
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}
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}
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TheLocation = *precedent;
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}
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//=======================================================================
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//function : Dump
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//purpose :
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//=======================================================================
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void math_NewtonMinimum::Dump(Standard_OStream& o) const
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{
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o<< "math_Newton Optimisation: ";
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o << " Done =" << Done << std::endl;
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o << " Status = " << (Standard_Integer)TheStatus << std::endl;
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o << " Location Vector = " << Location() << std::endl;
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o << " Minimum value = "<< Minimum()<< std::endl;
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o << " Previous value = "<< PreviousMinimum << std::endl;
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o << " Number of iterations = " <<NbIterations() << std::endl;
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o << " Convexity = " << Convex << std::endl;
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o << " Eigen Value = " << MinEigenValue << std::endl;
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}
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