mirror of https://github.com/lammps/lammps.git
261 lines
10 KiB
C++
261 lines
10 KiB
C++
/* -*- c++ -*- ----------------------------------------------------------
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*
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* *** Smooth Mach Dynamics ***
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*
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* This file is part of the USER-SMD package for LAMMPS.
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* Copyright (2014) Georg C. Ganzenmueller, georg.ganzenmueller@emi.fhg.de
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* Fraunhofer Ernst-Mach Institute for High-Speed Dynamics, EMI,
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* Eckerstrasse 4, D-79104 Freiburg i.Br, Germany.
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*
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* ----------------------------------------------------------------------- */
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#ifndef SMD_MATH_H
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#define SMD_MATH_H
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#include <Eigen/Eigen>
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#include <iostream>
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namespace SMD_Math {
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static inline void LimitDoubleMagnitude(double &x, const double limit) {
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/*
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* if |x| exceeds limit, set x to limit with the sign of x
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*/
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if (fabs(x) > limit) { // limit delVdotDelR to a fraction of speed of sound
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x = limit * copysign(1.0, x);
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}
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}
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/*
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* deviator of a tensor
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*/
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static inline Eigen::Matrix3d Deviator(const Eigen::Matrix3d M) {
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Eigen::Matrix3d eye;
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eye.setIdentity();
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eye *= M.trace() / 3.0;
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return M - eye;
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}
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/*
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* Polar Decomposition M = R * T
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* where R is a rotation and T a pure translation/stretch matrix.
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*
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* The decomposition is achieved using SVD, i.e. M = U S V^T,
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* where U = R V and S is diagonal.
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*
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*
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* For any physically admissible deformation gradient, the determinant of R must equal +1.
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* However, scenerios can arise, where the particles interpenetrate and cause inversion, leading to a determinant of R equal to -1.
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* In this case, the inversion direction is heuristically identified with the eigenvector of the smallest entry of S, which should work for most cases.
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* The sign of this corresponding eigenvalue is flipped, the original matrix M is recomputed using the flipped S, and the rotation and translation matrices are
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* obtained again from an SVD. The rotation should proper now, i.e., det(R) = +1.
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*/
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static inline bool PolDec(Eigen::Matrix3d M, Eigen::Matrix3d &R, Eigen::Matrix3d &T, bool scaleF) {
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Eigen::JacobiSVD<Eigen::Matrix3d> svd(M, Eigen::ComputeFullU | Eigen::ComputeFullV); // SVD(A) = U S V*
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Eigen::Vector3d S_eigenvalues = svd.singularValues();
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Eigen::Matrix3d S = svd.singularValues().asDiagonal();
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Eigen::Matrix3d U = svd.matrixU();
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Eigen::Matrix3d V = svd.matrixV();
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Eigen::Matrix3d eye;
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eye.setIdentity();
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// now do polar decomposition into M = R * T, where R is rotation
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// and T is translation matrix
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R = U * V.transpose();
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T = V * S * V.transpose();
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if (R.determinant() < 0.0) { // this is an improper rotation
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// identify the smallest entry in S and flip its sign
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int imin;
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S_eigenvalues.minCoeff(&imin);
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S(imin, imin) *= -1.0;
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R = M * V * S.inverse() * V.transpose(); // recompute R using flipped stretch eigenvalues
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}
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/*
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* scale S to avoid small principal strains
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*/
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if (scaleF) {
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double min = 0.3; // 0.3^2 = 0.09, should suffice for most problems
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double max = 2.0;
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for (int i = 0; i < 3; i++) {
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if (S(i, i) < min) {
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S(i, i) = min;
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} else if (S(i, i) > max) {
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S(i, i) = max;
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}
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}
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T = V * S * V.transpose();
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}
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if (R.determinant() > 0.0) {
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return true;
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} else {
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return false;
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}
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}
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/*
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* Pseudo-inverse via SVD
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*/
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static inline void pseudo_inverse_SVD(Eigen::Matrix3d &M) {
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Eigen::JacobiSVD<Eigen::Matrix3d> svd(M, Eigen::ComputeFullU); // one Eigevector base is sufficient because matrix is square and symmetric
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Eigen::Vector3d singularValuesInv;
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Eigen::Vector3d singularValues = svd.singularValues();
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double pinvtoler = 1.0e-16; // 2d machining example goes unstable if this value is increased (1.0e-16).
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for (int row = 0; row < 3; row++) {
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if (singularValues(row) > pinvtoler) {
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singularValuesInv(row) = 1.0 / singularValues(row);
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} else {
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singularValuesInv(row) = 0.0;
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}
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}
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M = svd.matrixU() * singularValuesInv.asDiagonal() * svd.matrixU().transpose();
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}
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/*
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* test if two matrices are equal
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*/
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static inline double TestMatricesEqual(Eigen::Matrix3d A, Eigen::Matrix3d B, double eps) {
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Eigen::Matrix3d diff;
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diff = A - B;
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double norm = diff.norm();
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if (norm > eps) {
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std::cout << "Matrices A and B are not equal! The L2-norm difference is: " << norm << "\n"
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<< "Here is matrix A:\n" << A << "\n"
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<< "Here is matrix B:\n" << B << std::endl;
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}
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return norm;
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}
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/* ----------------------------------------------------------------------
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Limit eigenvalues of a matrix to upper and lower bounds.
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------------------------------------------------------------------------- */
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static inline Eigen::Matrix3d LimitEigenvalues(Eigen::Matrix3d S, double limitEigenvalue) {
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/*
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* compute Eigenvalues of matrix S
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*/
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Eigen::SelfAdjointEigenSolver < Eigen::Matrix3d > es;
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es.compute(S);
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double max_eigenvalue = es.eigenvalues().maxCoeff();
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double min_eigenvalue = es.eigenvalues().minCoeff();
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double amax_eigenvalue = fabs(max_eigenvalue);
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double amin_eigenvalue = fabs(min_eigenvalue);
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if ((amax_eigenvalue > limitEigenvalue) || (amin_eigenvalue > limitEigenvalue)) {
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if (amax_eigenvalue > amin_eigenvalue) { // need to scale with max_eigenvalue
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double scale = amax_eigenvalue / limitEigenvalue;
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Eigen::Matrix3d V = es.eigenvectors();
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Eigen::Matrix3d S_diag = V.inverse() * S * V; // diagonalized input matrix
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S_diag /= scale;
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Eigen::Matrix3d S_scaled = V * S_diag * V.inverse(); // undiagonalize matrix
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return S_scaled;
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} else { // need to scale using min_eigenvalue
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double scale = amin_eigenvalue / limitEigenvalue;
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Eigen::Matrix3d V = es.eigenvectors();
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Eigen::Matrix3d S_diag = V.inverse() * S * V; // diagonalized input matrix
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S_diag /= scale;
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Eigen::Matrix3d S_scaled = V * S_diag * V.inverse(); // undiagonalize matrix
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return S_scaled;
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}
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} else { // limiting does not apply
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return S;
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}
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}
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static inline bool LimitMinMaxEigenvalues(Eigen::Matrix3d &S, double min, double max) {
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/*
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* compute Eigenvalues of matrix S
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*/
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Eigen::SelfAdjointEigenSolver < Eigen::Matrix3d > es;
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es.compute(S);
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if ((es.eigenvalues().maxCoeff() > max) || (es.eigenvalues().minCoeff() < min)) {
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Eigen::Matrix3d S_diag = es.eigenvalues().asDiagonal();
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Eigen::Matrix3d V = es.eigenvectors();
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for (int i = 0; i < 3; i++) {
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if (S_diag(i, i) < min) {
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//printf("limiting eigenvalue %f --> %f\n", S_diag(i, i), min);
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//printf("these are the eigenvalues of U: %f %f %f\n", es.eigenvalues()(0), es.eigenvalues()(1), es.eigenvalues()(2));
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S_diag(i, i) = min;
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} else if (S_diag(i, i) > max) {
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//printf("limiting eigenvalue %f --> %f\n", S_diag(i, i), max);
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S_diag(i, i) = max;
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}
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}
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S = V * S_diag * V.inverse(); // undiagonalize matrix
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return true;
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} else {
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return false;
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}
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}
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static inline void reconstruct_rank_deficient_shape_matrix(Eigen::Matrix3d &K) {
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Eigen::JacobiSVD<Eigen::Matrix3d> svd(K, Eigen::ComputeFullU | Eigen::ComputeFullV);
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Eigen::Vector3d singularValues = svd.singularValues();
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for (int i = 0; i < 3; i++) {
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if (singularValues(i) < 1.0e-8) {
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singularValues(i) = 1.0;
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}
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}
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// int imin;
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// double minev = singularValues.minCoeff(&imin);
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//
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// printf("min eigenvalue=%f has index %d\n", minev, imin);
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// Vector3d singularVec = U.col(0).cross(U.col(1));
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// cout << "the eigenvalues are " << endl << singularValues << endl;
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// cout << "the singular vector is " << endl << singularVec << endl;
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//
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// // reconstruct original K
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//
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// singularValues(2) = 1.0;
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K = svd.matrixU() * singularValues.asDiagonal() * svd.matrixV().transpose();
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//cout << "the reconstructed K is " << endl << K << endl;
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//exit(1);
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}
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/* ----------------------------------------------------------------------
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helper functions for crack_exclude
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------------------------------------------------------------------------- */
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static inline bool IsOnSegment(double xi, double yi, double xj, double yj, double xk, double yk) {
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return (xi <= xk || xj <= xk) && (xk <= xi || xk <= xj) && (yi <= yk || yj <= yk) && (yk <= yi || yk <= yj);
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}
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static inline char ComputeDirection(double xi, double yi, double xj, double yj, double xk, double yk) {
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double a = (xk - xi) * (yj - yi);
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double b = (xj - xi) * (yk - yi);
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return a < b ? -1.0 : a > b ? 1.0 : 0;
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}
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/** Do line segments (x1, y1)--(x2, y2) and (x3, y3)--(x4, y4) intersect? */
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static inline bool DoLineSegmentsIntersect(double x1, double y1, double x2, double y2, double x3, double y3, double x4, double y4) {
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char d1 = ComputeDirection(x3, y3, x4, y4, x1, y1);
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char d2 = ComputeDirection(x3, y3, x4, y4, x2, y2);
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char d3 = ComputeDirection(x1, y1, x2, y2, x3, y3);
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char d4 = ComputeDirection(x1, y1, x2, y2, x4, y4);
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return (((d1 > 0 && d2 < 0) || (d1 < 0 && d2 > 0)) && ((d3 > 0 && d4 < 0) || (d3 < 0 && d4 > 0)))
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|| (d1 == 0 && IsOnSegment(x3, y3, x4, y4, x1, y1)) || (d2 == 0 && IsOnSegment(x3, y3, x4, y4, x2, y2))
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|| (d3 == 0 && IsOnSegment(x1, y1, x2, y2, x3, y3)) || (d4 == 0 && IsOnSegment(x1, y1, x2, y2, x4, y4));
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}
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}
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#endif /* SMD_MATH_H_ */
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