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Building igl statically and moving to the dep scripts
Fixing dep build script on Windows and removing some warnings. Use bundled igl by default. Not building with the dependency scripts if not explicitly stated. This way, it will stay in Fix the libigl patch to include C source files in header only mode.
This commit is contained in:
tamasmeszaros
parent
89e39e3895
commit
2ae2672ee9
1095 changed files with 181 additions and 5 deletions
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// This file is part of libigl, a simple c++ geometry processing library.
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//
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// Copyright (C) 2015 Alec Jacobson <alecjacobson@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla Public License
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// v. 2.0. If a copy of the MPL was not distributed with this file, You can
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// obtain one at http://mozilla.org/MPL/2.0/.
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#include "linprog.h"
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#include "slice.h"
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#include "slice_into.h"
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#include "find.h"
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#include "colon.h"
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#include <iostream>
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//#define IGL_LINPROG_VERBOSE
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IGL_INLINE bool igl::linprog(
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const Eigen::VectorXd & c,
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const Eigen::MatrixXd & _A,
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const Eigen::VectorXd & b,
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const int k,
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Eigen::VectorXd & x)
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{
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// This is a very literal translation of
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// http://www.mathworks.com/matlabcentral/fileexchange/2166-introduction-to-linear-algebra/content/strang/linprog.m
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using namespace Eigen;
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using namespace std;
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bool success = true;
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// number of constraints
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const int m = _A.rows();
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// number of original variables
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const int n = _A.cols();
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// number of iterations
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int it = 0;
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// maximum number of iterations
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//const int MAXIT = 10*m;
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const int MAXIT = 100*m;
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// residual tolerance
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const double tol = 1e-10;
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const auto & sign = [](const Eigen::VectorXd & B) -> Eigen::VectorXd
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{
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Eigen::VectorXd Bsign(B.size());
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for(int i = 0;i<B.size();i++)
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{
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Bsign(i) = B(i)>0?1:(B(i)<0?-1:0);
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}
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return Bsign;
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};
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// initial (inverse) basis matrix
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VectorXd Dv = sign(sign(b).array()+0.5);
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Dv.head(k).setConstant(1.);
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MatrixXd D = Dv.asDiagonal();
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// Incorporate slack variables
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MatrixXd A(_A.rows(),_A.cols()+D.cols());
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A<<_A,D;
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// Initial basis
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VectorXi B = igl::colon<int>(n,n+m-1);
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// non-basis, may turn out that vector<> would be better here
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VectorXi N = igl::colon<int>(0,n-1);
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int j;
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double bmin = b.minCoeff(&j);
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int phase;
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VectorXd xb;
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VectorXd s;
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VectorXi J;
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if(k>0 && bmin<0)
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{
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phase = 1;
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xb = VectorXd::Ones(m);
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// super cost
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s.resize(n+m+1);
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s<<VectorXd::Zero(n+k),VectorXd::Ones(m-k+1);
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N.resize(n+1);
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N<<igl::colon<int>(0,n-1),B(j);
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J.resize(B.size()-1);
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// [0 1 2 3 4]
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// ^
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// [0 1]
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// [3 4]
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J.head(j) = B.head(j);
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J.tail(B.size()-j-1) = B.tail(B.size()-j-1);
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B(j) = n+m;
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MatrixXd AJ;
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igl::slice(A,J,2,AJ);
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const VectorXd a = b - AJ.rowwise().sum();
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{
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MatrixXd old_A = A;
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A.resize(A.rows(),A.cols()+a.cols());
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A<<old_A,a;
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}
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D.col(j) = -a/a(j);
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D(j,j) = 1./a(j);
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}else if(k==m)
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{
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phase = 2;
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xb = b;
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s.resize(c.size()+m);
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// cost function
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s<<c,VectorXd::Zero(m);
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}else //k = 0 or bmin >=0
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{
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phase = 1;
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xb = b.array().abs();
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s.resize(n+m);
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// super cost
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s<<VectorXd::Zero(n+k),VectorXd::Ones(m-k);
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}
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while(phase<3)
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{
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double df = -1;
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int t = std::numeric_limits<int>::max();
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// Lagrange mutipliers fro Ax=b
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VectorXd yb = D.transpose() * igl::slice(s,B);
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while(true)
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{
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if(MAXIT>0 && it>=MAXIT)
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{
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#ifdef IGL_LINPROG_VERBOSE
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cerr<<"linprog: warning! maximum iterations without convergence."<<endl;
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#endif
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success = false;
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break;
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}
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// no freedom for minimization
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if(N.size() == 0)
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{
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break;
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}
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// reduced costs
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VectorXd sN = igl::slice(s,N);
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MatrixXd AN = igl::slice(A,N,2);
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VectorXd r = sN - AN.transpose() * yb;
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int q;
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// determine new basic variable
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double rmin = r.minCoeff(&q);
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// optimal! infinity norm
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if(rmin>=-tol*(sN.array().abs().maxCoeff()+1))
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{
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break;
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}
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// increment iteration count
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it++;
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// apply Bland's rule to avoid cycling
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if(df>=0)
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{
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if(MAXIT == -1)
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{
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#ifdef IGL_LINPROG_VERBOSE
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cerr<<"linprog: warning! degenerate vertex"<<endl;
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#endif
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success = false;
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}
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igl::find((r.array()<0).eval(),J);
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double Nq = igl::slice(N,J).minCoeff();
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// again seems like q is assumed to be a scalar though matlab code
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// could produce a vector for multiple matches
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(N.array()==Nq).cast<int>().maxCoeff(&q);
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}
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VectorXd d = D*A.col(N(q));
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VectorXi I;
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igl::find((d.array()>tol).eval(),I);
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if(I.size() == 0)
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{
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#ifdef IGL_LINPROG_VERBOSE
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cerr<<"linprog: warning! solution is unbounded"<<endl;
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#endif
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// This seems dubious:
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it=-it;
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success = false;
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break;
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}
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VectorXd xbd = igl::slice(xb,I).array()/igl::slice(d,I).array();
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// new use of r
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int p;
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{
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double r;
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r = xbd.minCoeff(&p);
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p = I(p);
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// apply Bland's rule to avoid cycling
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if(df>=0)
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{
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igl::find((xbd.array()==r).eval(),J);
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double Bp = igl::slice(B,igl::slice(I,J)).minCoeff();
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// idiotic way of finding index in B of Bp
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// code down the line seems to assume p is a scalar though the matlab
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// code could find a vector of matches)
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(B.array()==Bp).cast<int>().maxCoeff(&p);
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}
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// update x
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xb -= r*d;
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xb(p) = r;
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// change in f
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df = r*rmin;
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}
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// row vector
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RowVectorXd v = D.row(p)/d(p);
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yb += v.transpose() * (s(N(q)) - d.transpose()*igl::slice(s,B));
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d(p)-=1;
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// update inverse basis matrix
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D = D - d*v;
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t = B(p);
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B(p) = N(q);
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if(t>(n+k-1))
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{
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// remove qth entry from N
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VectorXi old_N = N;
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N.resize(N.size()-1);
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N.head(q) = old_N.head(q);
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N.head(q) = old_N.head(q);
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N.tail(old_N.size()-q-1) = old_N.tail(old_N.size()-q-1);
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}else
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{
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N(q) = t;
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}
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}
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// iterative refinement
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xb = (xb+D*(b-igl::slice(A,B,2)*xb)).eval();
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// must be due to rounding
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VectorXi I;
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igl::find((xb.array()<0).eval(),I);
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if(I.size()>0)
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{
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// so correct
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VectorXd Z = VectorXd::Zero(I.size(),1);
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igl::slice_into(Z,I,xb);
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}
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// B, xb,n,m,res=A(:,B)*xb-b
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if(phase == 2 || it<0)
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{
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break;
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}
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if(xb.transpose()*igl::slice(s,B) > tol)
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{
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it = -it;
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#ifdef IGL_LINPROG_VERBOSE
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cerr<<"linprog: warning, no feasible solution"<<endl;
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#endif
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success = false;
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break;
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}
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// re-initialize for Phase 2
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phase = phase+1;
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s*=1e6*c.array().abs().maxCoeff();
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s.head(n) = c;
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}
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x.resize(std::max(B.maxCoeff()+1,n));
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igl::slice_into(xb,B,x);
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x = x.head(n).eval();
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return success;
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}
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IGL_INLINE bool igl::linprog(
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const Eigen::VectorXd & f,
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const Eigen::MatrixXd & A,
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const Eigen::VectorXd & b,
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const Eigen::MatrixXd & B,
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const Eigen::VectorXd & c,
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Eigen::VectorXd & x)
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{
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using namespace Eigen;
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using namespace std;
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const int m = A.rows();
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const int n = A.cols();
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const int p = B.rows();
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MatrixXd Im = MatrixXd::Identity(m,m);
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MatrixXd AS(m,n+m);
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AS<<A,Im;
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MatrixXd bS = b.array().abs();
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for(int i = 0;i<m;i++)
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{
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const auto & sign = [](double x)->double
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{
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return (x<0?-1:(x>0?1:0));
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};
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AS.row(i) *= sign(b(i));
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}
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MatrixXd In = MatrixXd::Identity(n,n);
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MatrixXd P(n+m,2*n+m);
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P<< In, -In, MatrixXd::Zero(n,m),
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MatrixXd::Zero(m,2*n), Im;
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MatrixXd ASP = AS*P;
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MatrixXd BSP(0,2*n+m);
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if(p>0)
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{
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MatrixXd BS(p,2*n);
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BS<<B,MatrixXd::Zero(p,n);
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BSP = BS*P;
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}
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VectorXd fSP = VectorXd::Ones(2*n+m);
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fSP.head(2*n) = P.block(0,0,n,2*n).transpose()*f;
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const VectorXd & cc = fSP;
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MatrixXd AA(m+p,2*n+m);
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AA<<ASP,BSP;
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VectorXd bb(m+p);
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bb<<bS,c;
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VectorXd xxs;
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bool ret = linprog(cc,AA,bb,0,xxs);
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x = P.block(0,0,n,2*n+m)*xxs;
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return ret;
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}
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