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quadprog++ and Eigen library test
Dependencies: mbed Eigen FastPWM
quadprog.cpp
- Committer:
- jsoh91
- Date:
- 2019-09-24
- Revision:
- 2:e843c1b0b25c
File content as of revision 2:e843c1b0b25c:
/* File $Id: QuadProg++.cc 232 2007-06-21 12:29:00Z digasper $ Author: Luca Di Gaspero DIEGM - University of Udine, Italy luca.digaspero@uniud.it http://www.diegm.uniud.it/digaspero/ This software may be modified and distributed under the terms of the MIT license. See the LICENSE file for details. */ #include <iostream> #include <algorithm> #include <cmath> #include <limits> #include <sstream> #include <stdexcept> #include "quadprog.h" #include "math.h" //#define TRACE_SOLVER namespace quadprogpp { // Utility functions for updating some data needed by the solution method void compute_d(Vector<double>& d, const Matrix<double>& J, const Vector<double>& np); void update_z(Vector<double>& z, const Matrix<double>& J, const Vector<double>& d, int iq); void update_r(const Matrix<double>& R, Vector<double>& r, const Vector<double>& d, int iq); bool add_constraint(Matrix<double>& R, Matrix<double>& J, Vector<double>& d, int& iq, double& rnorm); void delete_constraint(Matrix<double>& R, Matrix<double>& J, Vector<int>& A, Vector<double>& u, int n, int p, int& iq, int l); // Utility functions for computing the Cholesky decomposition and solving // linear systems void cholesky_decomposition(Matrix<double>& A); void cholesky_solve(const Matrix<double>& L, Vector<double>& x, const Vector<double>& b); void forward_elimination(const Matrix<double>& L, Vector<double>& y, const Vector<double>& b); void backward_elimination(const Matrix<double>& U, Vector<double>& x, const Vector<double>& y); // Utility functions for computing the scalar product and the euclidean // distance between two numbers double scalar_product(const Vector<double>& x, const Vector<double>& y); double distance(double a, double b); // Utility functions for printing vectors and matrices void print_matrix(char* name, const Matrix<double>& A, int n = -1, int m = -1); template<typename T> void print_vector(char* name, const Vector<T>& v, int n = -1); // The Solving function, implementing the Goldfarb-Idnani method double solve_quadprog(Matrix<double>& G, Vector<double>& g0, const Matrix<double>& CE, const Vector<double>& ce0, const Matrix<double>& CI, const Vector<double>& ci0, Vector<double>& x) { std::ostringstream msg; int n = G.ncols(), p = CE.ncols(), m = CI.ncols(); if (G.nrows() != n) { msg << "The matrix G is not a squared matrix (" << G.nrows() << " x " << G.ncols() << ")"; // throw std::logic_error(msg.str()); } if (CE.nrows() != n) { msg << "The matrix CE is incompatible (incorrect number of rows " << CE.nrows() << " , expecting " << n << ")"; // throw std::logic_error(msg.str()); } if (ce0.size() != p) { msg << "The vector ce0 is incompatible (incorrect dimension " << ce0.size() << ", expecting " << p << ")"; // throw std::logic_error(msg.str()); } if (CI.nrows() != n) { msg << "The matrix CI is incompatible (incorrect number of rows " << CI.nrows() << " , expecting " << n << ")"; // throw std::logic_error(msg.str()); } if (ci0.size() != m) { msg << "The vector ci0 is incompatible (incorrect dimension " << ci0.size() << ", expecting " << m << ")"; // throw std::logic_error(msg.str()); } x.resize(n); register int i, j, k, l; /* indices */ int ip; // this is the index of the constraint to be added to the active set Matrix<double> R(n, n), J(n, n); Vector<double> s(m + p), z(n), r(m + p), d(n), np(n), u(m + p), x_old(n), u_old(m + p); double f_value, psi, c1, c2, sum, ss, R_norm; double inf; if (std::numeric_limits<double>::has_infinity) inf = std::numeric_limits<double>::infinity(); else inf = 1.0E300; double t, t1, t2; /* t is the step lenght, which is the minimum of the partial step length t1 * and the full step length t2 */ Vector<int> A(m + p), A_old(m + p), iai(m + p); int q, iq, iter = 0; Vector<bool> iaexcl(m + p); /* p is the number of equality constraints */ /* m is the number of inequality constraints */ q = 0; /* size of the active set A (containing the indices of the active constraints) */ #ifdef TRACE_SOLVER std::cout << std::endl << "Starting solve_quadprog" << std::endl; print_matrix("G", G); print_vector("g0", g0); print_matrix("CE", CE); print_vector("ce0", ce0); print_matrix("CI", CI); print_vector("ci0", ci0); #endif /* * Preprocessing phase */ /* compute the trace of the original matrix G */ c1 = 0.0; for (i = 0; i < n; i++) { c1 += G[i][i]; } /* decompose the matrix G in the form L^T L */ cholesky_decomposition(G); #ifdef TRACE_SOLVER print_matrix("G", G); #endif /* initialize the matrix R */ for (i = 0; i < n; i++) { d[i] = 0.0; for (j = 0; j < n; j++) R[i][j] = 0.0; } R_norm = 1.0; /* this variable will hold the norm of the matrix R */ /* compute the inverse of the factorized matrix G^-1, this is the initial value for H */ c2 = 0.0; for (i = 0; i < n; i++) { d[i] = 1.0; forward_elimination(G, z, d); for (j = 0; j < n; j++) J[i][j] = z[j]; c2 += z[i]; d[i] = 0.0; } #ifdef TRACE_SOLVER print_matrix("J", J); #endif /* c1 * c2 is an estimate for cond(G) */ /* * Find the unconstrained minimizer of the quadratic form 0.5 * x G x + g0 x * this is a feasible point in the dual space * x = G^-1 * g0 */ cholesky_solve(G, x, g0); for (i = 0; i < n; i++) x[i] = -x[i]; /* and compute the current solution value */ f_value = 0.5 * scalar_product(g0, x); #ifdef TRACE_SOLVER std::cout << "Unconstrained solution: " << f_value << std::endl; print_vector("x", x); #endif /* Add equality constraints to the working set A */ iq = 0; for (i = 0; i < p; i++) { for (j = 0; j < n; j++) np[j] = CE[j][i]; compute_d(d, J, np); update_z(z, J, d, iq); update_r(R, r, d, iq); #ifdef TRACE_SOLVER print_matrix("R", R, n, iq); print_vector("z", z); print_vector("r", r, iq); print_vector("d", d); #endif /* compute full step length t2: i.e., the minimum step in primal space s.t. the contraint becomes feasible */ t2 = 0.0; if (fabs(scalar_product(z, z)) > std::numeric_limits<double>::epsilon()) // i.e. z != 0 t2 = (-scalar_product(np, x) - ce0[i]) / scalar_product(z, np); /* set x = x + t2 * z */ for (k = 0; k < n; k++) x[k] += t2 * z[k]; /* set u = u+ */ u[iq] = t2; for (k = 0; k < iq; k++) u[k] -= t2 * r[k]; /* compute the new solution value */ f_value += 0.5 * (t2 * t2) * scalar_product(z, np); A[i] = -i - 1; if (!add_constraint(R, J, d, iq, R_norm)) { // Equality constraints are linearly dependent // throw std::runtime_error("Constraints are linearly dependent"); return f_value; } } /* set iai = K \ A */ for (i = 0; i < m; i++) iai[i] = i; l1: iter++; #ifdef TRACE_SOLVER print_vector("x", x); #endif /* step 1: choose a violated constraint */ for (i = p; i < iq; i++) { ip = A[i]; iai[ip] = -1; } /* compute s[x] = ci^T * x + ci0 for all elements of K \ A */ ss = 0.0; psi = 0.0; /* this value will contain the sum of all infeasibilities */ ip = 0; /* ip will be the index of the chosen violated constraint */ for (i = 0; i < m; i++) { iaexcl[i] = true; sum = 0.0; for (j = 0; j < n; j++) sum += CI[j][i] * x[j]; sum += ci0[i]; s[i] = sum; psi += std::min(0.0, sum); } #ifdef TRACE_SOLVER print_vector("s", s, m); #endif if (fabs(psi) <= m * std::numeric_limits<double>::epsilon() * c1 * c2* 100.0) { /* numerically there are not infeasibilities anymore */ q = iq; return f_value; } /* save old values for u and A */ for (i = 0; i < iq; i++) { u_old[i] = u[i]; A_old[i] = A[i]; } /* and for x */ for (i = 0; i < n; i++) x_old[i] = x[i]; l2: /* Step 2: check for feasibility and determine a new S-pair */ for (i = 0; i < m; i++) { if (s[i] < ss && iai[i] != -1 && iaexcl[i]) { ss = s[i]; ip = i; } } if (ss >= 0.0) { q = iq; return f_value; } /* set np = n[ip] */ for (i = 0; i < n; i++) np[i] = CI[i][ip]; /* set u = [u 0]^T */ u[iq] = 0.0; /* add ip to the active set A */ A[iq] = ip; #ifdef TRACE_SOLVER std::cout << "Trying with constraint " << ip << std::endl; print_vector("np", np); #endif l2a:/* Step 2a: determine step direction */ /* compute z = H np: the step direction in the primal space (through J, see the paper) */ compute_d(d, J, np); update_z(z, J, d, iq); /* compute N* np (if q > 0): the negative of the step direction in the dual space */ update_r(R, r, d, iq); #ifdef TRACE_SOLVER std::cout << "Step direction z" << std::endl; print_vector("z", z); print_vector("r", r, iq + 1); print_vector("u", u, iq + 1); print_vector("d", d); print_vector("A", A, iq + 1); #endif /* Step 2b: compute step length */ l = 0; /* Compute t1: partial step length (maximum step in dual space without violating dual feasibility */ t1 = inf; /* +inf */ /* find the index l s.t. it reaches the minimum of u+[x] / r */ for (k = p; k < iq; k++) { if (r[k] > 0.0) { if (u[k] / r[k] < t1) { t1 = u[k] / r[k]; l = A[k]; } } } /* Compute t2: full step length (minimum step in primal space such that the constraint ip becomes feasible */ if (fabs(scalar_product(z, z)) > std::numeric_limits<double>::epsilon()) { // i.e. z != 0 t2 = -s[ip] / scalar_product(z, np); if (t2 < 0) // patch suggested by Takano Akio for handling numerical inconsistencies t2 = inf; } else t2 = inf; /* +inf */ /* the step is chosen as the minimum of t1 and t2 */ t = std::min(t1, t2); #ifdef TRACE_SOLVER std::cout << "Step sizes: " << t << " (t1 = " << t1 << ", t2 = " << t2 << ") "; #endif /* Step 2c: determine new S-pair and take step: */ /* case (i): no step in primal or dual space */ if (t >= inf) { /* QPP is infeasible */ // FIXME: unbounded to raise q = iq; return inf; } /* case (ii): step in dual space */ if (t2 >= inf) { /* set u = u + t * [-r 1] and drop constraint l from the active set A */ for (k = 0; k < iq; k++) u[k] -= t * r[k]; u[iq] += t; iai[l] = l; delete_constraint(R, J, A, u, n, p, iq, l); #ifdef TRACE_SOLVER std::cout << " in dual space: " << f_value << std::endl; print_vector("x", x); print_vector("z", z); print_vector("A", A, iq + 1); #endif goto l2a; } /* case (iii): step in primal and dual space */ /* set x = x + t * z */ for (k = 0; k < n; k++) x[k] += t * z[k]; /* update the solution value */ f_value += t * scalar_product(z, np) * (0.5 * t + u[iq]); /* u = u + t * [-r 1] */ for (k = 0; k < iq; k++) u[k] -= t * r[k]; u[iq] += t; #ifdef TRACE_SOLVER std::cout << " in both spaces: " << f_value << std::endl; print_vector("x", x); print_vector("u", u, iq + 1); print_vector("r", r, iq + 1); print_vector("A", A, iq + 1); #endif if (fabs(t - t2) < std::numeric_limits<double>::epsilon()) { #ifdef TRACE_SOLVER std::cout << "Full step has taken " << t << std::endl; print_vector("x", x); #endif /* full step has taken */ /* add constraint ip to the active set*/ if (!add_constraint(R, J, d, iq, R_norm)) { iaexcl[ip] = false; delete_constraint(R, J, A, u, n, p, iq, ip); #ifdef TRACE_SOLVER print_matrix("R", R); print_vector("A", A, iq); print_vector("iai", iai); #endif for (i = 0; i < m; i++) iai[i] = i; for (i = p; i < iq; i++) { A[i] = A_old[i]; u[i] = u_old[i]; iai[A[i]] = -1; } for (i = 0; i < n; i++) x[i] = x_old[i]; goto l2; /* go to step 2 */ } else iai[ip] = -1; #ifdef TRACE_SOLVER print_matrix("R", R); print_vector("A", A, iq); print_vector("iai", iai); #endif goto l1; } /* a patial step has taken */ #ifdef TRACE_SOLVER std::cout << "Partial step has taken " << t << std::endl; print_vector("x", x); #endif /* drop constraint l */ iai[l] = l; delete_constraint(R, J, A, u, n, p, iq, l); #ifdef TRACE_SOLVER print_matrix("R", R); print_vector("A", A, iq); #endif /* update s[ip] = CI * x + ci0 */ sum = 0.0; for (k = 0; k < n; k++) sum += CI[k][ip] * x[k]; s[ip] = sum + ci0[ip]; #ifdef TRACE_SOLVER print_vector("s", s, m); #endif goto l2a; } inline void compute_d(Vector<double>& d, const Matrix<double>& J, const Vector<double>& np) { register int i, j, n = d.size(); register double sum; /* compute d = H^T * np */ for (i = 0; i < n; i++) { sum = 0.0; for (j = 0; j < n; j++) sum += J[j][i] * np[j]; d[i] = sum; } } inline void update_z(Vector<double>& z, const Matrix<double>& J, const Vector<double>& d, int iq) { register int i, j, n = z.size(); /* setting of z = H * d */ for (i = 0; i < n; i++) { z[i] = 0.0; for (j = iq; j < n; j++) z[i] += J[i][j] * d[j]; } } inline void update_r(const Matrix<double>& R, Vector<double>& r, const Vector<double>& d, int iq) { register int i, j, n = d.size(); register double sum; /* setting of r = R^-1 d */ for (i = iq - 1; i >= 0; i--) { sum = 0.0; for (j = i + 1; j < iq; j++) sum += R[i][j] * r[j]; r[i] = (d[i] - sum) / R[i][i]; } } bool add_constraint(Matrix<double>& R, Matrix<double>& J, Vector<double>& d, int& iq, double& R_norm) { int n = d.size(); #ifdef TRACE_SOLVER std::cout << "Add constraint " << iq << '/'; #endif register int i, j, k; double cc, ss, h, t1, t2, xny; /* we have to find the Givens rotation which will reduce the element d[j] to zero. if it is already zero we don't have to do anything, except of decreasing j */ for (j = n - 1; j >= iq + 1; j--) { /* The Givens rotation is done with the matrix (cc cs, cs -cc). If cc is one, then element (j) of d is zero compared with element (j - 1). Hence we don't have to do anything. If cc is zero, then we just have to switch column (j) and column (j - 1) of J. Since we only switch columns in J, we have to be careful how we update d depending on the sign of gs. Otherwise we have to apply the Givens rotation to these columns. The i - 1 element of d has to be updated to h. */ cc = d[j - 1]; ss = d[j]; h = distance(cc, ss); if (fabs(h) < std::numeric_limits<double>::epsilon()) // h == 0 continue; d[j] = 0.0; ss = ss / h; cc = cc / h; if (cc < 0.0) { cc = -cc; ss = -ss; d[j - 1] = -h; } else d[j - 1] = h; xny = ss / (1.0 + cc); for (k = 0; k < n; k++) { t1 = J[k][j - 1]; t2 = J[k][j]; J[k][j - 1] = t1 * cc + t2 * ss; J[k][j] = xny * (t1 + J[k][j - 1]) - t2; } } /* update the number of constraints added*/ iq++; /* To update R we have to put the iq components of the d vector into column iq - 1 of R */ for (i = 0; i < iq; i++) R[i][iq - 1] = d[i]; #ifdef TRACE_SOLVER std::cout << iq << std::endl; print_matrix("R", R, iq, iq); print_matrix("J", J); print_vector("d", d, iq); #endif if (fabs(d[iq - 1]) <= std::numeric_limits<double>::epsilon() * R_norm) { // problem degenerate return false; } R_norm = std::max<double>(R_norm, fabs(d[iq - 1])); return true; } void delete_constraint(Matrix<double>& R, Matrix<double>& J, Vector<int>& A, Vector<double>& u, int n, int p, int& iq, int l) { #ifdef TRACE_SOLVER std::cout << "Delete constraint " << l << ' ' << iq; #endif register int i, j, k, qq = -1; // just to prevent warnings from smart compilers double cc, ss, h, xny, t1, t2; /* Find the index qq for active constraint l to be removed */ for (i = p; i < iq; i++) if (A[i] == l) { qq = i; break; } /* remove the constraint from the active set and the duals */ for (i = qq; i < iq - 1; i++) { A[i] = A[i + 1]; u[i] = u[i + 1]; for (j = 0; j < n; j++) R[j][i] = R[j][i + 1]; } A[iq - 1] = A[iq]; u[iq - 1] = u[iq]; A[iq] = 0; u[iq] = 0.0; for (j = 0; j < iq; j++) R[j][iq - 1] = 0.0; /* constraint has been fully removed */ iq--; #ifdef TRACE_SOLVER std::cout << '/' << iq << std::endl; #endif if (iq == 0) return; for (j = qq; j < iq; j++) { cc = R[j][j]; ss = R[j + 1][j]; h = distance(cc, ss); if (fabs(h) < std::numeric_limits<double>::epsilon()) // h == 0 continue; cc = cc / h; ss = ss / h; R[j + 1][j] = 0.0; if (cc < 0.0) { R[j][j] = -h; cc = -cc; ss = -ss; } else R[j][j] = h; xny = ss / (1.0 + cc); for (k = j + 1; k < iq; k++) { t1 = R[j][k]; t2 = R[j + 1][k]; R[j][k] = t1 * cc + t2 * ss; R[j + 1][k] = xny * (t1 + R[j][k]) - t2; } for (k = 0; k < n; k++) { t1 = J[k][j]; t2 = J[k][j + 1]; J[k][j] = t1 * cc + t2 * ss; J[k][j + 1] = xny * (J[k][j] + t1) - t2; } } } inline double distance(double a, double b) { register double a1, b1, t; a1 = fabs(a); b1 = fabs(b); if (a1 > b1) { t = (b1 / a1); return a1 * sqrt(1.0 + t * t); } else if (b1 > a1) { t = (a1 / b1); return b1 * sqrt(1.0 + t * t); } return a1 * sqrt(2.0); } inline double scalar_product(const Vector<double>& x, const Vector<double>& y) { register int i, n = x.size(); register double sum; sum = 0.0; for (i = 0; i < n; i++) sum += x[i] * y[i]; return sum; } void cholesky_decomposition(Matrix<double>& A) { register int i, j, k, n = A.nrows(); register double sum; for (i = 0; i < n; i++) { for (j = i; j < n; j++) { sum = A[i][j]; for (k = i - 1; k >= 0; k--) sum -= A[i][k]*A[j][k]; if (i == j) { if (sum <= 0.0) { std::ostringstream os; // raise error print_matrix("A", A); os << "Error in cholesky decomposition, sum: " << sum; // throw std::logic_error(os.str()); // exit(-1); } A[i][i] = sqrt(sum); } else A[j][i] = sum / A[i][i]; } for (k = i + 1; k < n; k++) A[i][k] = A[k][i]; } } void cholesky_solve(const Matrix<double>& L, Vector<double>& x, const Vector<double>& b) { int n = L.nrows(); Vector<double> y(n); /* Solve L * y = b */ forward_elimination(L, y, b); /* Solve L^T * x = y */ backward_elimination(L, x, y); } inline void forward_elimination(const Matrix<double>& L, Vector<double>& y, const Vector<double>& b) { register int i, j, n = L.nrows(); y[0] = b[0] / L[0][0]; for (i = 1; i < n; i++) { y[i] = b[i]; for (j = 0; j < i; j++) y[i] -= L[i][j] * y[j]; y[i] = y[i] / L[i][i]; } } inline void backward_elimination(const Matrix<double>& U, Vector<double>& x, const Vector<double>& y) { register int i, j, n = U.nrows(); x[n - 1] = y[n - 1] / U[n - 1][n - 1]; for (i = n - 2; i >= 0; i--) { x[i] = y[i]; for (j = i + 1; j < n; j++) x[i] -= U[i][j] * x[j]; x[i] = x[i] / U[i][i]; } } void print_matrix(char* name, const Matrix<double>& A, int n, int m) { std::ostringstream s; std::string t; if (n == -1) n = A.nrows(); if (m == -1) m = A.ncols(); s << name << ": " << std::endl; for (int i = 0; i < n; i++) { s << " "; for (int j = 0; j < m; j++) s << A[i][j] << ", "; s << std::endl; } t = s.str(); t = t.substr(0, t.size() - 3); // To remove the trailing space, comma and newline std::cout << t << std::endl; } template<typename T> void print_vector(char* name, const Vector<T>& v, int n) { std::ostringstream s; std::string t; if (n == -1) n = v.size(); s << name << ": " << std::endl << " "; for (int i = 0; i < n; i++) { s << v[i] << ", "; } t = s.str(); t = t.substr(0, t.size() - 2); // To remove the trailing space and comma std::cout << t << std::endl; } } // namespace quadprogpp