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/*
* vim: ts=4 sw=4 et tw=0 wm=0
*
* libcola - A library providing force-directed network layout using the
* stress-majorization method subject to separation constraints.
*
* Copyright (C) 2006-2008 Monash University
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
* See the file LICENSE.LGPL distributed with the library.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
*
*/
#ifndef _GRADIENT_PROJECTION_H
#define _GRADIENT_PROJECTION_H
#include <iostream>
#include <cmath>
#include <valarray>
#include "libvpsc/solve_VPSC.h"
#include "libvpsc/variable.h"
#include "libvpsc/constraint.h"
#include "libvpsc/rectangle.h"
#include "libcola/commondefs.h"
#include "libcola/compound_constraints.h"
#include "libcola/cluster.h"
#include "libcola/sparse_matrix.h"
#ifdef MOSEK_AVAILABLE
#include "libvpsc/mosek_quad_solve.h"
#endif
namespace straightener {
class Node;
}
namespace cola {
enum SolveWithMosek { Off, Inner, Outer };
class GradientProjection {
public:
/**
* GradientProjection solves a linear system
* Qx=b
* subject to separation constraints.
* The usual use-case is with a dense square matrix (denseQ).
* However, certain CompoundConstraints and also clusters add dummy
* variables and simple goal terms which populate a sparse matrix
* sparseQ (constructed in this constructor).
* A motivated person could rewrite the constructor to allow
* arbitrary sparse terms (if they had a use for it).
*/
GradientProjection(
const vpsc::Dim k,
std::valarray<double> *denseQ,
const double tol,
const unsigned max_iterations,
CompoundConstraints const *ccs,
UnsatisfiableConstraintInfos *unsatisfiableConstraints,
NonOverlapConstraintsMode nonOverlapConstraints = None,
RootCluster* clusterHierarchy = nullptr,
vpsc::Rectangles* rs = nullptr,
const bool scaling = false,
SolveWithMosek solveWithMosek = Off);
static void dumpSquareMatrix(std::valarray<double> const &L) {
unsigned n=static_cast<unsigned>(floor(sqrt(static_cast<double>(L.size()))));
printf("Matrix %dX%d\n{",n,n);
for(unsigned i=0;i<n;i++) {
printf("{");
for(unsigned j=0;j<n;j++) {
char c=j==n-1?'}':',';
printf("%f%c",1. * L[i*n+j],c);
}
char c=i==n-1?'}':',';
printf("%c\n",c);
}
}
unsigned getNumStaticVars() const {
return numStaticVars;
}
~GradientProjection() {
//destroyVPSC(solver);
for(vpsc::Constraints::iterator i(gcs.begin()); i!=gcs.end(); i++) {
delete *i;
}
gcs.clear();
for(unsigned i=0;i<vars.size();i++) {
delete vars[i];
}
}
unsigned solve(std::valarray<double> const & b, std::valarray<double> & x);
void unfixPos(unsigned i) {
if(vars[i]->fixedDesiredPosition) {
vars[i]->weight=1;
vars[i]->fixedDesiredPosition=false;
}
}
void fixPos(const unsigned i,const double pos) {
vars[i]->weight=100000.;
vars[i]->desiredPosition=pos;
vars[i]->fixedDesiredPosition=true;
}
vpsc::Dim getDimension() const {
return k;
}
void straighten(
cola::SparseMatrix const * Q,
std::vector<SeparationConstraint*> const & ccs,
std::vector<straightener::Node*> const & snodes);
std::valarray<double> const & getFullResult() const {
return result;
}
private:
vpsc::IncSolver* setupVPSC();
double computeCost(std::valarray<double> const &b,
std::valarray<double> const &x) const;
double computeSteepestDescentVector(
std::valarray<double> const &b, std::valarray<double> const &place,
std::valarray<double> &g) const;
double computeScaledSteepestDescentVector(
std::valarray<double> const &b, std::valarray<double> const &place,
std::valarray<double> &g) const;
double computeStepSize(
std::valarray<double> const & g, std::valarray<double> const & d) const;
bool runSolver(std::valarray<double> & result);
void destroyVPSC(vpsc::IncSolver *vpsc);
vpsc::Dim k;
unsigned numStaticVars; // number of variables that persist
// throughout iterations
const unsigned denseSize; // denseQ has denseSize^2 entries
std::valarray<double> *denseQ; // dense square graph laplacian matrix
std::valarray<double> scaledDenseQ; // scaled dense square graph laplacian matrix
std::vector<vpsc::Rectangle*>* rs;
CompoundConstraints const *ccs;
UnsatisfiableConstraintInfos *unsatisfiableConstraints;
NonOverlapConstraintsMode nonOverlapConstraints;
Cluster* clusterHierarchy;
double tolerance;
unsigned max_iterations;
cola::SparseMatrix const * sparseQ; // sparse components of goal function
vpsc::Variables vars; // all variables
// computations
vpsc::Constraints gcs; /* global constraints - persist throughout all
iterations */
vpsc::Constraints lcs; /* local constraints - only for current iteration */
vpsc::Constraints cs; /* working list of constraints: gcs +lcs */
std::valarray<double> result;
#ifdef MOSEK_AVAILABLE
MosekEnv* menv;
#endif
vpsc::IncSolver* solver;
SolveWithMosek solveWithMosek;
const bool scaling;
std::vector<OrthogonalEdgeConstraint*> orthogonalEdges;
};
} // namespace cola
#endif /* _GRADIENT_PROJECTION_H */
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