From cca66b9ec4e494c1d919bff0f71a820d8afab1fa Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Sun, 7 Apr 2024 20:24:48 +0200
Subject: Adding upstream version 1.2.2.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 .../adaptagrams/libcola/gradient_projection.cpp    | 482 +++++++++++++++++++++
 1 file changed, 482 insertions(+)
 create mode 100644 src/3rdparty/adaptagrams/libcola/gradient_projection.cpp

(limited to 'src/3rdparty/adaptagrams/libcola/gradient_projection.cpp')

diff --git a/src/3rdparty/adaptagrams/libcola/gradient_projection.cpp b/src/3rdparty/adaptagrams/libcola/gradient_projection.cpp
new file mode 100644
index 0000000..b53e876
--- /dev/null
+++ b/src/3rdparty/adaptagrams/libcola/gradient_projection.cpp
@@ -0,0 +1,482 @@
+/*
+ * 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.
+ *
+*/
+
+/**********************************************************
+ *
+ * Solve a quadratic function f(X) = X' A X + b X
+ * subject to a set of separation constraints cs
+ *
+ **********************************************************/
+
+#include <iostream>
+#include <cmath>
+#include <ctime>
+
+#include <libvpsc/solve_VPSC.h>
+#include <libvpsc/variable.h>
+#include <libvpsc/constraint.h>
+#include <libvpsc/assertions.h>
+
+#include "libcola/cluster.h"
+#include "libcola/gradient_projection.h"
+#include "libcola/straightener.h"
+#include "libcola/commondefs.h"
+//#define CHECK_CONVERGENCE_BY_COST 1
+
+
+using namespace std;
+using namespace vpsc;
+namespace cola {
+GradientProjection::GradientProjection(
+    const Dim k,
+    std::valarray<double> *denseQ,
+    const double tol,
+    const unsigned max_iterations,
+    CompoundConstraints const *ccs,
+    UnsatisfiableConstraintInfos *unsatisfiableConstraints,
+    NonOverlapConstraintsMode nonOverlapConstraints,
+    RootCluster* clusterHierarchy,
+    vpsc::Rectangles* rs,
+    const bool scaling,
+    SolveWithMosek solveWithMosek) 
+        : k(k), 
+          denseSize(static_cast<unsigned>((floor(sqrt(static_cast<double>(denseQ->size())))))),
+          denseQ(denseQ), 
+          rs(rs),
+          ccs(ccs),
+          unsatisfiableConstraints(unsatisfiableConstraints),
+          nonOverlapConstraints(nonOverlapConstraints),
+          clusterHierarchy(clusterHierarchy),
+          tolerance(tol), 
+          max_iterations(max_iterations),
+          sparseQ(nullptr),
+          solveWithMosek(solveWithMosek),
+          scaling(scaling)
+{
+    //printf("GP Instance: scaling=%d, mosek=%d\n",scaling,solveWithMosek);
+    for(unsigned i=0;i<denseSize;i++) {
+        vars.push_back(new vpsc::Variable(i,1,1));
+    }
+    if(scaling) {
+        scaledDenseQ.resize(denseSize*denseSize);
+        for(unsigned i=0;i<denseSize;i++) {
+            vars[i]->scale=1./sqrt(fabs((*denseQ)[i*denseSize+i]));
+            // XXX: Scale can sometimes be set to infinity here when 
+            //      there are nodes not connected to any other node.
+            //      Thus we just set the scale for such a variable to 1.
+            if (!std::isfinite(vars[i]->scale))
+            {
+                vars[i]->scale = 1;
+            }
+        }
+        // the following computes S'QS for Q=denseQ
+        // and S is diagonal matrix of scale factors
+        for(unsigned i=0;i<denseSize;i++) {
+            for(unsigned j=0;j<denseSize;j++) {
+                scaledDenseQ[i*denseSize+j]=(*denseQ)[i*denseSize+j]*vars[i]->scale
+                    *vars[j]->scale;
+            }
+        }
+        this->denseQ = &scaledDenseQ;
+    }
+    //dumpSquareMatrix(*this->denseQ);
+    //dumpSquareMatrix(scaledDenseQ);
+
+    if(ccs) {
+        for(CompoundConstraints::const_iterator c=ccs->begin();
+                c!=ccs->end();++c) {
+            (*c)->generateVariables(k, vars);
+            OrthogonalEdgeConstraint* e=dynamic_cast<OrthogonalEdgeConstraint*>(*c);
+            if(e) {
+                orthogonalEdges.push_back(e);
+            }
+        }
+        for(CompoundConstraints::const_iterator c=ccs->begin();
+                c!=ccs->end();++c) {
+            (*c)->generateSeparationConstraints(k, vars, gcs, *rs);
+        }
+    }
+    /*
+    if(clusterHierarchy) {
+        clusterHierarchy->createVars(k,*rs,vars);
+    }
+    */
+    numStaticVars=vars.size();
+    //solver=setupVPSC();
+}
+static inline double dotProd(valarray<double> const & a, valarray<double> const & b) {
+    double p = 0;
+    for (unsigned i=0; i<a.size(); i++) {
+        p += a[i]*b[i];
+    }
+    return p;
+}
+double GradientProjection::computeCost(
+        valarray<double> const &b,
+        valarray<double> const &x) const {
+    // computes cost = 2 b x - x A x
+    double cost = 2. * dotProd(b,x);
+    valarray<double> Ax(x.size());
+    for (unsigned i=0; i<denseSize; i++) {
+        Ax[i] = 0;
+        for (unsigned j=0; j<denseSize; j++) {
+            Ax[i] += (*denseQ)[i*denseSize+j]*x[j];
+        }
+    }
+    if(sparseQ) {
+        valarray<double> r(x.size());
+        sparseQ->rightMultiply(x,r);
+        Ax+=r;
+    }
+    return cost - dotProd(x,Ax);
+}
+
+double GradientProjection::computeSteepestDescentVector(
+        valarray<double> const &b,
+        valarray<double> const &x,
+        valarray<double> &g) const {
+    // find steepest descent direction
+    //  g = 2 ( b - A x )
+    //    where: A = denseQ + sparseQ
+    //  g = 2 ( b - denseQ x) - 2 sparseQ x
+    //
+    //  except the 2s don't matter because we compute 
+    //  the optimal stepsize anyway
+    COLA_ASSERT(x.size()==b.size() && b.size()==g.size());
+    g = b;
+    for (unsigned i=0; i<denseSize; i++) {
+        for (unsigned j=0; j<denseSize; j++) {
+            g[i] -= (*denseQ)[i*denseSize+j]*x[j];
+        }
+    }
+    // sparse part:
+    if(sparseQ) {
+        valarray<double> r(x.size());
+        sparseQ->rightMultiply(x,r);
+        g-=r;
+    }
+    return computeStepSize(g,g);
+}
+// compute optimal step size along descent vector d relative to
+// a gradient related vector g 
+//    stepsize = ( g' d ) / ( d' A d )
+double GradientProjection::computeStepSize(
+        valarray<double> const & g, valarray<double> const & d) const {
+    COLA_ASSERT(g.size()==d.size());
+    valarray<double> Ad;
+    if(sparseQ) {
+        Ad.resize(g.size());
+        sparseQ->rightMultiply(d,Ad);
+    }
+    double const numerator = dotProd(g, d);
+    double denominator = 0;
+    for (unsigned i=0; i<g.size(); i++) {
+        double r = sparseQ ? Ad[i] : 0;
+        if(i<denseSize) { for (unsigned j=0; j<denseSize; j++) {
+            r += (*denseQ)[i*denseSize+j] * d[j];
+        } }
+        denominator += r * d[i];
+    }
+    if(denominator==0) {
+        return 0;
+    }
+    return numerator/(2.*denominator);
+}
+
+bool GradientProjection::runSolver(valarray<double> & result) {
+    bool activeConstraints = false;
+    switch(solveWithMosek) {
+        case Off:
+            activeConstraints = solver->satisfy();
+            for (unsigned i=0;i<vars.size();i++) {
+                result[i]=vars[i]->finalPosition;
+            }
+            break;
+        case Inner:
+#ifdef MOSEK_AVAILABLE
+            float *ba=new float[vars.size()];
+            float *coords=new float[vars.size()];
+            for(unsigned i=0;i<vars.size();i++) {
+                ba[i]=vars[i]->desiredPosition;
+            }
+            mosek_quad_solve_sep(menv,ba,coords);
+            for(unsigned i=0;i<vars.size();i++) {
+                //printf("x[%d]=%f\n",i,coords[i]);
+                result[i]=coords[i];
+            }
+            delete [] ba;
+            delete [] coords;
+            break;
+#endif
+        default:
+            break;
+    }
+    return activeConstraints;
+}
+
+/*
+ * Use gradient-projection to solve an instance of
+ * the Variable Placement with Separation Constraints problem.
+ */
+unsigned GradientProjection::solve(
+        valarray<double> const &linearCoefficients, 
+        valarray<double> &x) {
+    COLA_ASSERT(linearCoefficients.size()==x.size());
+    COLA_ASSERT(x.size()==denseSize);
+    COLA_ASSERT(numStaticVars>=denseSize);
+    COLA_ASSERT(sparseQ==nullptr || 
+                (sparseQ!=nullptr && (vars.size()==sparseQ->rowSize())) );
+
+    if(max_iterations==0) return 0;
+
+    bool converged=false;
+
+    solver = setupVPSC();
+#ifdef MOSEK_AVAILABLE
+    if(solveWithMosek==Outer) {
+        float* ba=new float[vars.size()];
+        float* xa=new float[vars.size()];
+        for(unsigned i=0;i<vars.size();i++) {
+            ba[i]=-linearCoefficients[i];
+        }
+        mosek_quad_solve_sep(menv,ba,xa);
+        for(unsigned i=0;i<vars.size();i++) {
+            //printf("mosek result x[%d]=%f\n",i,xa[i]);
+            x[i]=xa[i];
+        }
+        delete [] ba;
+        delete [] xa;
+        return 1;
+    }
+#endif
+    // it may be that we have to consider dummy vars, which the caller didn't know
+    // about.  Thus vars.size() may not equal x.size()
+    unsigned n = vars.size();
+    valarray<double> b(n);
+    result.resize(n);
+    
+    // load desired positions into vars, note that we keep desired positions 
+    // already calculated for dummy vars
+    for (unsigned i=0;i<x.size();i++) {
+        COLA_ASSERT(!std::isnan(x[i]));
+        COLA_ASSERT(std::isfinite(x[i]));
+        b[i]=i<linearCoefficients.size()?linearCoefficients[i]:0;
+        result[i]=x[i];
+        if(scaling) {
+            b[i]*=vars[i]->scale;
+            result[i]/=vars[i]->scale;
+        } 
+        if(!vars[i]->fixedDesiredPosition) vars[i]->desiredPosition=result[i];
+    }
+    runSolver(result);
+        
+    valarray<double> g(n); /* gradient */
+    valarray<double> previous(n); /* stored positions */
+    valarray<double> d(n); /* actual descent vector */
+
+#ifdef CHECK_CONVERGENCE_BY_COST
+    double previousCost = DBL_MAX;
+#endif
+    unsigned counter=0;
+    double stepSize;
+    for (; counter<max_iterations&&!converged; counter++) {
+        previous=result;
+        stepSize=0;
+        double alpha=computeSteepestDescentVector(b,result,g);
+
+        //printf("Iteration[%d]\n",counter);
+        // move to new unconstrained position
+        for (unsigned i=0; i<n; i++) {
+            // dividing by variable weight is a cheap trick to make these
+            // weights mean something in terms of the descent vector
+            double step=alpha*g[i]/vars[i]->weight;
+            result[i]+=step;
+            //printf("   after unconstrained step: x[%d]=%f\n",i,result[i]);
+            stepSize+=step*step;
+            COLA_ASSERT(!std::isnan(result[i]));
+            COLA_ASSERT(std::isfinite(result[i]));
+            if(!vars[i]->fixedDesiredPosition) vars[i]->desiredPosition=result[i];
+        }
+
+        //project to constraint boundary
+        bool constrainedOptimum = false;
+        constrainedOptimum=runSolver(result);
+        stepSize=0;
+        for (unsigned i=0;i<n;i++) {
+            double step = previous[i]-result[i];
+            stepSize+=step*step;
+        }
+        //constrainedOptimum=false;
+        // beta seems, more often than not, to be >1!
+        if(constrainedOptimum) {
+            // The following step limits the step-size in the feasible
+            // direction
+            d = result - previous;
+            const double beta = 0.5*computeStepSize(g, d);
+            // beta > 1.0 takes us back outside the feasible region
+            // beta < 0 clearly not useful and may happen due to numerical imp.
+            //printf("beta=%f\n",beta);
+            if(beta>0&&beta<0.99999) {
+                stepSize=0;
+                for (unsigned i=0; i<n; i++) {
+                    double step=beta*d[i];
+                    result[i]=previous[i]+step;
+                    stepSize+=step*step;
+                }
+            }
+        }
+#ifdef CHECK_CONVERGENCE_BY_COST
+        /* This would be the slow way to detect convergence */
+        //if(counter%2) {
+            double cost = computeCost(b,result);
+            printf("     gp[%d] %.15f %.15f\n",counter,previousCost,cost);
+            //COLA_ASSERT(previousCost>cost);
+            if(fabs(previousCost - cost) < tolerance) {
+                converged = true;
+            }
+            previousCost = cost;
+        //}
+#else
+        if(stepSize<tolerance) converged = true; 
+#endif
+    }
+    //printf("GP[%d] converged after %d iterations.\n",k,counter);
+    for(unsigned i=0;i<x.size();i++) {
+        x[i]=result[i];
+        if(scaling) {
+            x[i]*=vars[i]->scale;
+        }
+    }
+    destroyVPSC(solver);
+    return counter;
+}
+// Setup an instance of the Variable Placement with Separation Constraints
+// for one iteration.
+// Generate transient local constraints --- such as non-overlap constraints 
+// --- that are only relevant to one iteration, and merge these with the
+// global constraint list (including alignment constraints,
+// dir-edge constraints, containment constraints, etc).
+IncSolver* GradientProjection::setupVPSC() {
+    if(nonOverlapConstraints!=None) {
+        if(clusterHierarchy) {
+            //printf("Setup up cluster constraints, dim=%d--------------\n",k);
+            //clusterHierarchy->generateNonOverlapConstraints(k,nonOverlapConstraints,*rs,vars,lcs);
+        } else {
+            for(vector<OrthogonalEdgeConstraint*>::iterator i=orthogonalEdges.begin();i!=orthogonalEdges.end();i++) {
+                OrthogonalEdgeConstraint* e=*i;
+                e->generateTopologyConstraints(k,*rs,vars,lcs);
+            }
+            if(k==HORIZONTAL) {
+                // Make rectangles a little bit wider when processing horizontally so that any overlap
+                // resolved horizontally is strictly non-overlapping when processing vertically
+                Rectangle::setXBorder(0.0001);
+                // use rs->size() rather than n because some of the variables may
+                // be dummy vars with no corresponding rectangle
+                generateXConstraints(*rs,vars,lcs,nonOverlapConstraints==Both?true:false); 
+                Rectangle::setXBorder(0);
+            } else {
+                generateYConstraints(*rs,vars,lcs); 
+            }
+        }
+    }
+    cs=gcs;
+    cs.insert(cs.end(),lcs.begin(),lcs.end());
+    switch(solveWithMosek) {
+        case Off:
+            break;
+#ifdef MOSEK_AVAILABLE
+        case Inner:
+            menv = mosek_init_sep_ls(vars.size(),cs);
+            break;
+        case Outer:
+            unsigned n = vars.size();
+            float* lap = new float[n*(n+1)/2];
+            unsigned k=0;
+            for(unsigned i=0;i<n;i++) {
+                for(unsigned j=i;j<n;j++) {
+                    lap[k]=(*denseQ)[i*n+j];
+                    k++;
+                }
+            }
+            menv = mosek_init_sep(lap,n,cs,1);
+            delete [] lap;
+            break;
+#endif
+        default:
+            break;
+    }
+    return new IncSolver(vars,cs);
+}
+void GradientProjection::destroyVPSC(IncSolver *vpsc) {
+    if(ccs) {
+        for(CompoundConstraints::const_iterator c=ccs->begin(); 
+                c!=ccs->end();++c) {
+            (*c)->updatePosition(vpsc::XDIM);
+            (*c)->updatePosition(vpsc::YDIM);
+        }
+    }
+    if(unsatisfiableConstraints) {
+        unsatisfiableConstraints->clear();
+        for(Constraints::iterator i=cs.begin();i!=cs.end();i++) {
+            Constraint* c=*i;
+            if(c->unsatisfiable) {
+                UnsatisfiableConstraintInfo *ci = new UnsatisfiableConstraintInfo(c);
+                unsatisfiableConstraints->push_back(ci);
+            }
+        }
+    }
+    if(clusterHierarchy) {
+        clusterHierarchy->computeBoundary(*rs);
+    }
+    if(sparseQ) {
+        for(unsigned i=numStaticVars;i<vars.size();i++) {
+            delete vars[i];
+        }
+        vars.resize(numStaticVars);
+        sparseQ=nullptr;
+    }
+    for(vector<Constraint*>::iterator i=lcs.begin();i!=lcs.end();i++) {
+        delete *i;
+    }
+    lcs.clear();
+    delete vpsc;
+#ifdef MOSEK_AVAILABLE
+    if(solveWithMosek!=Off) mosek_delete(menv);
+#endif
+}
+void GradientProjection::straighten(
+    cola::SparseMatrix const * Q, 
+    vector<SeparationConstraint*> const & cs,
+    vector<straightener::Node*> const & snodes) 
+{
+    COLA_ASSERT(Q->rowSize()==snodes.size());
+    COLA_ASSERT(vars.size()==numStaticVars);
+    sparseQ = Q;
+    for(unsigned i=numStaticVars;i<snodes.size();i++) {
+        Variable* v=new vpsc::Variable(i,snodes[i]->pos[k],1);
+        COLA_ASSERT(v->desiredPosition==snodes[i]->pos[k]);
+        vars.push_back(v);
+    }
+    COLA_ASSERT(lcs.size()==0);
+    for(vector<SeparationConstraint*>::const_iterator i=cs.begin();i!=cs.end();i++) {
+        (*i)->generateSeparationConstraints(k, vars, lcs, *rs); 
+    }
+}
+} // namespace cola
-- 
cgit v1.2.3