204 lines
5.9 KiB
C++
204 lines
5.9 KiB
C++
// 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 "biharmonic_coordinates.h"
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#include "cotmatrix.h"
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#include "sum.h"
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#include "massmatrix.h"
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#include "min_quad_with_fixed.h"
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#include "crouzeix_raviart_massmatrix.h"
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#include "crouzeix_raviart_cotmatrix.h"
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#include "normal_derivative.h"
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#include "on_boundary.h"
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#include <Eigen/Sparse>
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template <
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typename DerivedV,
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typename DerivedT,
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typename SType,
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typename DerivedW>
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IGL_INLINE bool igl::biharmonic_coordinates(
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const Eigen::PlainObjectBase<DerivedV> & V,
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const Eigen::PlainObjectBase<DerivedT> & T,
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const std::vector<std::vector<SType> > & S,
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Eigen::PlainObjectBase<DerivedW> & W)
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{
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return biharmonic_coordinates(V,T,S,2,W);
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}
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template <
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typename DerivedV,
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typename DerivedT,
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typename SType,
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typename DerivedW>
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IGL_INLINE bool igl::biharmonic_coordinates(
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const Eigen::PlainObjectBase<DerivedV> & V,
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const Eigen::PlainObjectBase<DerivedT> & T,
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const std::vector<std::vector<SType> > & S,
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const int k,
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Eigen::PlainObjectBase<DerivedW> & W)
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{
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using namespace Eigen;
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using namespace std;
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// This is not the most efficient way to build A, but follows "Linear
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// Subspace Design for Real-Time Shape Deformation" [Wang et al. 2015].
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SparseMatrix<double> A;
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{
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DiagonalMatrix<double,Dynamic> Minv;
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SparseMatrix<double> L,K;
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Array<bool,Dynamic,Dynamic> C;
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{
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Array<bool,Dynamic,1> I;
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on_boundary(T,I,C);
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}
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#ifdef false
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// Version described in paper is "wrong"
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// http://www.cs.toronto.edu/~jacobson/images/error-in-linear-subspace-design-for-real-time-shape-deformation-2017-wang-et-al.pdf
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SparseMatrix<double> N,Z,M;
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normal_derivative(V,T,N);
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{
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std::vector<Triplet<double> >ZIJV;
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for(int t =0;t<T.rows();t++)
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{
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for(int f =0;f<T.cols();f++)
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{
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if(C(t,f))
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{
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const int i = t+f*T.rows();
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for(int c = 1;c<T.cols();c++)
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{
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ZIJV.emplace_back(T(t,(f+c)%T.cols()),i,1);
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}
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}
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}
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}
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Z.resize(V.rows(),N.rows());
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Z.setFromTriplets(ZIJV.begin(),ZIJV.end());
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N = (Z*N).eval();
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}
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cotmatrix(V,T,L);
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K = N+L;
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massmatrix(V,T,MASSMATRIX_TYPE_DEFAULT,M);
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// normalize
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M /= ((VectorXd)M.diagonal()).array().abs().maxCoeff();
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Minv =
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((VectorXd)M.diagonal().array().inverse()).asDiagonal();
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#else
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Eigen::SparseMatrix<double> M;
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Eigen::MatrixXi E;
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Eigen::VectorXi EMAP;
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crouzeix_raviart_massmatrix(V,T,M,E,EMAP);
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crouzeix_raviart_cotmatrix(V,T,E,EMAP,L);
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// Ad #E by #V facet-vertex incidence matrix
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Eigen::SparseMatrix<double> Ad(E.rows(),V.rows());
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{
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std::vector<Eigen::Triplet<double> > AIJV(E.size());
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for(int e = 0;e<E.rows();e++)
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{
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for(int c = 0;c<E.cols();c++)
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{
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AIJV[e+c*E.rows()] = Eigen::Triplet<double>(e,E(e,c),1);
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}
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}
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Ad.setFromTriplets(AIJV.begin(),AIJV.end());
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}
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// Degrees
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Eigen::VectorXd De;
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sum(Ad,2,De);
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Eigen::DiagonalMatrix<double,Eigen::Dynamic> De_diag =
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De.array().inverse().matrix().asDiagonal();
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K = L*(De_diag*Ad);
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// normalize
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M /= ((VectorXd)M.diagonal()).array().abs().maxCoeff();
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Minv = ((VectorXd)M.diagonal().array().inverse()).asDiagonal();
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// kill boundary edges
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for(int f = 0;f<T.rows();f++)
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{
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for(int c = 0;c<T.cols();c++)
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{
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if(C(f,c))
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{
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const int e = EMAP(f+T.rows()*c);
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Minv.diagonal()(e) = 0;
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}
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}
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}
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#endif
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switch(k)
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{
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default:
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assert(false && "unsupported");
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case 2:
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// For C1 smoothness in 2D, one should use bi-harmonic
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A = K.transpose() * (Minv * K);
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break;
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case 3:
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// For C1 smoothness in 3D, one should use tri-harmonic
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A = K.transpose() * (Minv * (-L * (Minv * K)));
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break;
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}
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}
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// Vertices in point handles
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const size_t mp =
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count_if(S.begin(),S.end(),[](const vector<int> & h){return h.size()==1;});
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// number of region handles
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const size_t r = S.size()-mp;
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// Vertices in region handles
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size_t mr = 0;
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for(const auto & h : S)
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{
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if(h.size() > 1)
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{
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mr += h.size();
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}
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}
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const size_t dim = T.cols()-1;
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// Might as well be dense... I think...
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MatrixXd J = MatrixXd::Zero(mp+mr,mp+r*(dim+1));
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VectorXi b(mp+mr);
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MatrixXd H(mp+r*(dim+1),dim);
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{
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int v = 0;
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int c = 0;
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for(int h = 0;h<S.size();h++)
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{
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if(S[h].size()==1)
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{
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H.row(c) = V.block(S[h][0],0,1,dim);
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J(v,c++) = 1;
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b(v) = S[h][0];
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v++;
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}else
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{
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assert(S[h].size() >= dim+1);
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for(int p = 0;p<S[h].size();p++)
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{
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for(int d = 0;d<dim;d++)
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{
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J(v,c+d) = V(S[h][p],d);
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}
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J(v,c+dim) = 1;
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b(v) = S[h][p];
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v++;
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}
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H.block(c,0,dim+1,dim).setIdentity();
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c+=dim+1;
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}
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}
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}
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// minimize ½ W' A W'
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// subject to W(b,:) = J
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return min_quad_with_fixed(
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A,VectorXd::Zero(A.rows()).eval(),b,J,SparseMatrix<double>(),VectorXd(),true,W);
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}
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#ifdef IGL_STATIC_LIBRARY
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// Explicit template instantiation
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template bool igl::biharmonic_coordinates<Eigen::Matrix<double, -1, -1, 0, -1, -1>, Eigen::Matrix<int, -1, -1, 0, -1, -1>, int, Eigen::Matrix<double, -1, -1, 0, -1, -1> >(Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0, -1, -1> > const&, Eigen::PlainObjectBase<Eigen::Matrix<int, -1, -1, 0, -1, -1> > const&, std::vector<std::vector<int, std::allocator<int> >, std::allocator<std::vector<int, std::allocator<int> > > > const&, int, Eigen::PlainObjectBase<Eigen::Matrix<double, -1, -1, 0, -1, -1> >&);
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#endif
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