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Diffstat (limited to 'test/monniaux/glpk-4.65/src/draft/glpipm.c')
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1 files changed, 1144 insertions, 0 deletions
diff --git a/test/monniaux/glpk-4.65/src/draft/glpipm.c b/test/monniaux/glpk-4.65/src/draft/glpipm.c new file mode 100644 index 00000000..2b3a8176 --- /dev/null +++ b/test/monniaux/glpk-4.65/src/draft/glpipm.c @@ -0,0 +1,1144 @@ +/* glpipm.c */ + +/*********************************************************************** +* This code is part of GLPK (GNU Linear Programming Kit). +* +* Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, +* 2009, 2010, 2011, 2013 Andrew Makhorin, Department for Applied +* Informatics, Moscow Aviation Institute, Moscow, Russia. All rights +* reserved. E-mail: <mao@gnu.org>. +* +* GLPK is free software: you can redistribute it and/or modify it +* under the terms of the GNU General Public License as published by +* the Free Software Foundation, either version 3 of the License, or +* (at your option) any later version. +* +* GLPK 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. See the GNU General Public +* License for more details. +* +* You should have received a copy of the GNU General Public License +* along with GLPK. If not, see <http://www.gnu.org/licenses/>. +***********************************************************************/ + +#include "env.h" +#include "glpipm.h" +#include "glpmat.h" + +#define ITER_MAX 100 +/* maximal number of iterations */ + +struct csa +{ /* common storage area */ + /*--------------------------------------------------------------*/ + /* LP data */ + int m; + /* number of rows (equality constraints) */ + int n; + /* number of columns (structural variables) */ + int *A_ptr; /* int A_ptr[1+m+1]; */ + int *A_ind; /* int A_ind[A_ptr[m+1]]; */ + double *A_val; /* double A_val[A_ptr[m+1]]; */ + /* mxn-matrix A in storage-by-rows format */ + double *b; /* double b[1+m]; */ + /* m-vector b of right-hand sides */ + double *c; /* double c[1+n]; */ + /* n-vector c of objective coefficients; c[0] is constant term of + the objective function */ + /*--------------------------------------------------------------*/ + /* LP solution */ + double *x; /* double x[1+n]; */ + double *y; /* double y[1+m]; */ + double *z; /* double z[1+n]; */ + /* current point in primal-dual space; the best point on exit */ + /*--------------------------------------------------------------*/ + /* control parameters */ + const glp_iptcp *parm; + /*--------------------------------------------------------------*/ + /* working arrays and variables */ + double *D; /* double D[1+n]; */ + /* diagonal nxn-matrix D = X*inv(Z), where X = diag(x[j]) and + Z = diag(z[j]) */ + int *P; /* int P[1+m+m]; */ + /* permutation mxm-matrix P used to minimize fill-in in Cholesky + factorization */ + int *S_ptr; /* int S_ptr[1+m+1]; */ + int *S_ind; /* int S_ind[S_ptr[m+1]]; */ + double *S_val; /* double S_val[S_ptr[m+1]]; */ + double *S_diag; /* double S_diag[1+m]; */ + /* symmetric mxm-matrix S = P*A*D*A'*P' whose upper triangular + part without diagonal elements is stored in S_ptr, S_ind, and + S_val in storage-by-rows format, diagonal elements are stored + in S_diag */ + int *U_ptr; /* int U_ptr[1+m+1]; */ + int *U_ind; /* int U_ind[U_ptr[m+1]]; */ + double *U_val; /* double U_val[U_ptr[m+1]]; */ + double *U_diag; /* double U_diag[1+m]; */ + /* upper triangular mxm-matrix U defining Cholesky factorization + S = U'*U; its non-diagonal elements are stored in U_ptr, U_ind, + U_val in storage-by-rows format, diagonal elements are stored + in U_diag */ + int iter; + /* iteration number (0, 1, 2, ...); iter = 0 corresponds to the + initial point */ + double obj; + /* current value of the objective function */ + double rpi; + /* relative primal infeasibility rpi = ||A*x-b||/(1+||b||) */ + double rdi; + /* relative dual infeasibility rdi = ||A'*y+z-c||/(1+||c||) */ + double gap; + /* primal-dual gap = |c'*x-b'*y|/(1+|c'*x|) which is a relative + difference between primal and dual objective functions */ + double phi; + /* merit function phi = ||A*x-b||/max(1,||b||) + + + ||A'*y+z-c||/max(1,||c||) + + + |c'*x-b'*y|/max(1,||b||,||c||) */ + double mu; + /* duality measure mu = x'*z/n (used as barrier parameter) */ + double rmu; + /* rmu = max(||A*x-b||,||A'*y+z-c||)/mu */ + double rmu0; + /* the initial value of rmu on iteration 0 */ + double *phi_min; /* double phi_min[1+ITER_MAX]; */ + /* phi_min[k] = min(phi[k]), where phi[k] is the value of phi on + k-th iteration, 0 <= k <= iter */ + int best_iter; + /* iteration number, on which the value of phi reached its best + (minimal) value */ + double *best_x; /* double best_x[1+n]; */ + double *best_y; /* double best_y[1+m]; */ + double *best_z; /* double best_z[1+n]; */ + /* best point (in the sense of the merit function phi) which has + been reached on iteration iter_best */ + double best_obj; + /* objective value at the best point */ + double *dx_aff; /* double dx_aff[1+n]; */ + double *dy_aff; /* double dy_aff[1+m]; */ + double *dz_aff; /* double dz_aff[1+n]; */ + /* affine scaling direction */ + double alfa_aff_p, alfa_aff_d; + /* maximal primal and dual stepsizes in affine scaling direction, + on which x and z are still non-negative */ + double mu_aff; + /* duality measure mu_aff = x_aff'*z_aff/n in the boundary point + x_aff' = x+alfa_aff_p*dx_aff, z_aff' = z+alfa_aff_d*dz_aff */ + double sigma; + /* Mehrotra's heuristic parameter (0 <= sigma <= 1) */ + double *dx_cc; /* double dx_cc[1+n]; */ + double *dy_cc; /* double dy_cc[1+m]; */ + double *dz_cc; /* double dz_cc[1+n]; */ + /* centering corrector direction */ + double *dx; /* double dx[1+n]; */ + double *dy; /* double dy[1+m]; */ + double *dz; /* double dz[1+n]; */ + /* final combined direction dx = dx_aff+dx_cc, dy = dy_aff+dy_cc, + dz = dz_aff+dz_cc */ + double alfa_max_p; + double alfa_max_d; + /* maximal primal and dual stepsizes in combined direction, on + which x and z are still non-negative */ +}; + +/*********************************************************************** +* initialize - allocate and initialize common storage area +* +* This routine allocates and initializes the common storage area (CSA) +* used by interior-point method routines. */ + +static void initialize(struct csa *csa) +{ int m = csa->m; + int n = csa->n; + int i; + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Matrix A has %d non-zeros\n", csa->A_ptr[m+1]-1); + csa->D = xcalloc(1+n, sizeof(double)); + /* P := I */ + csa->P = xcalloc(1+m+m, sizeof(int)); + for (i = 1; i <= m; i++) csa->P[i] = csa->P[m+i] = i; + /* S := A*A', symbolically */ + csa->S_ptr = xcalloc(1+m+1, sizeof(int)); + csa->S_ind = adat_symbolic(m, n, csa->P, csa->A_ptr, csa->A_ind, + csa->S_ptr); + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Matrix S = A*A' has %d non-zeros (upper triangle)\n", + csa->S_ptr[m+1]-1 + m); + /* determine P using specified ordering algorithm */ + if (csa->parm->ord_alg == GLP_ORD_NONE) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Original ordering is being used\n"); + for (i = 1; i <= m; i++) + csa->P[i] = csa->P[m+i] = i; + } + else if (csa->parm->ord_alg == GLP_ORD_QMD) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Minimum degree ordering (QMD)...\n"); + min_degree(m, csa->S_ptr, csa->S_ind, csa->P); + } + else if (csa->parm->ord_alg == GLP_ORD_AMD) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Approximate minimum degree ordering (AMD)...\n"); + amd_order1(m, csa->S_ptr, csa->S_ind, csa->P); + } + else if (csa->parm->ord_alg == GLP_ORD_SYMAMD) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Approximate minimum degree ordering (SYMAMD)...\n") + ; + symamd_ord(m, csa->S_ptr, csa->S_ind, csa->P); + } + else + xassert(csa != csa); + /* S := P*A*A'*P', symbolically */ + xfree(csa->S_ind); + csa->S_ind = adat_symbolic(m, n, csa->P, csa->A_ptr, csa->A_ind, + csa->S_ptr); + csa->S_val = xcalloc(csa->S_ptr[m+1], sizeof(double)); + csa->S_diag = xcalloc(1+m, sizeof(double)); + /* compute Cholesky factorization S = U'*U, symbolically */ + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Computing Cholesky factorization S = L*L'...\n"); + csa->U_ptr = xcalloc(1+m+1, sizeof(int)); + csa->U_ind = chol_symbolic(m, csa->S_ptr, csa->S_ind, csa->U_ptr); + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Matrix L has %d non-zeros\n", csa->U_ptr[m+1]-1 + m); + csa->U_val = xcalloc(csa->U_ptr[m+1], sizeof(double)); + csa->U_diag = xcalloc(1+m, sizeof(double)); + csa->iter = 0; + csa->obj = 0.0; + csa->rpi = 0.0; + csa->rdi = 0.0; + csa->gap = 0.0; + csa->phi = 0.0; + csa->mu = 0.0; + csa->rmu = 0.0; + csa->rmu0 = 0.0; + csa->phi_min = xcalloc(1+ITER_MAX, sizeof(double)); + csa->best_iter = 0; + csa->best_x = xcalloc(1+n, sizeof(double)); + csa->best_y = xcalloc(1+m, sizeof(double)); + csa->best_z = xcalloc(1+n, sizeof(double)); + csa->best_obj = 0.0; + csa->dx_aff = xcalloc(1+n, sizeof(double)); + csa->dy_aff = xcalloc(1+m, sizeof(double)); + csa->dz_aff = xcalloc(1+n, sizeof(double)); + csa->alfa_aff_p = 0.0; + csa->alfa_aff_d = 0.0; + csa->mu_aff = 0.0; + csa->sigma = 0.0; + csa->dx_cc = xcalloc(1+n, sizeof(double)); + csa->dy_cc = xcalloc(1+m, sizeof(double)); + csa->dz_cc = xcalloc(1+n, sizeof(double)); + csa->dx = csa->dx_aff; + csa->dy = csa->dy_aff; + csa->dz = csa->dz_aff; + csa->alfa_max_p = 0.0; + csa->alfa_max_d = 0.0; + return; +} + +/*********************************************************************** +* A_by_vec - compute y = A*x +* +* This routine computes matrix-vector product y = A*x, where A is the +* constraint matrix. */ + +static void A_by_vec(struct csa *csa, double x[], double y[]) +{ /* compute y = A*x */ + int m = csa->m; + int *A_ptr = csa->A_ptr; + int *A_ind = csa->A_ind; + double *A_val = csa->A_val; + int i, t, beg, end; + double temp; + for (i = 1; i <= m; i++) + { temp = 0.0; + beg = A_ptr[i], end = A_ptr[i+1]; + for (t = beg; t < end; t++) temp += A_val[t] * x[A_ind[t]]; + y[i] = temp; + } + return; +} + +/*********************************************************************** +* AT_by_vec - compute y = A'*x +* +* This routine computes matrix-vector product y = A'*x, where A' is a +* matrix transposed to the constraint matrix A. */ + +static void AT_by_vec(struct csa *csa, double x[], double y[]) +{ /* compute y = A'*x, where A' is transposed to A */ + int m = csa->m; + int n = csa->n; + int *A_ptr = csa->A_ptr; + int *A_ind = csa->A_ind; + double *A_val = csa->A_val; + int i, j, t, beg, end; + double temp; + for (j = 1; j <= n; j++) y[j] = 0.0; + for (i = 1; i <= m; i++) + { temp = x[i]; + if (temp == 0.0) continue; + beg = A_ptr[i], end = A_ptr[i+1]; + for (t = beg; t < end; t++) y[A_ind[t]] += A_val[t] * temp; + } + return; +} + +/*********************************************************************** +* decomp_NE - numeric factorization of matrix S = P*A*D*A'*P' +* +* This routine implements numeric phase of Cholesky factorization of +* the matrix S = P*A*D*A'*P', which is a permuted matrix of the normal +* equation system. Matrix D is assumed to be already computed. */ + +static void decomp_NE(struct csa *csa) +{ adat_numeric(csa->m, csa->n, csa->P, csa->A_ptr, csa->A_ind, + csa->A_val, csa->D, csa->S_ptr, csa->S_ind, csa->S_val, + csa->S_diag); + chol_numeric(csa->m, csa->S_ptr, csa->S_ind, csa->S_val, + csa->S_diag, csa->U_ptr, csa->U_ind, csa->U_val, csa->U_diag); + return; +} + +/*********************************************************************** +* solve_NE - solve normal equation system +* +* This routine solves the normal equation system: +* +* A*D*A'*y = h. +* +* It is assumed that the matrix A*D*A' has been previously factorized +* by the routine decomp_NE. +* +* On entry the array y contains the vector of right-hand sides h. On +* exit this array contains the computed vector of unknowns y. +* +* Once the vector y has been computed the routine checks for numeric +* stability. If the residual vector: +* +* r = A*D*A'*y - h +* +* is relatively small, the routine returns zero, otherwise non-zero is +* returned. */ + +static int solve_NE(struct csa *csa, double y[]) +{ int m = csa->m; + int n = csa->n; + int *P = csa->P; + int i, j, ret = 0; + double *h, *r, *w; + /* save vector of right-hand sides h */ + h = xcalloc(1+m, sizeof(double)); + for (i = 1; i <= m; i++) h[i] = y[i]; + /* solve normal equation system (A*D*A')*y = h */ + /* since S = P*A*D*A'*P' = U'*U, then A*D*A' = P'*U'*U*P, so we + have inv(A*D*A') = P'*inv(U)*inv(U')*P */ + /* w := P*h */ + w = xcalloc(1+m, sizeof(double)); + for (i = 1; i <= m; i++) w[i] = y[P[i]]; + /* w := inv(U')*w */ + ut_solve(m, csa->U_ptr, csa->U_ind, csa->U_val, csa->U_diag, w); + /* w := inv(U)*w */ + u_solve(m, csa->U_ptr, csa->U_ind, csa->U_val, csa->U_diag, w); + /* y := P'*w */ + for (i = 1; i <= m; i++) y[i] = w[P[m+i]]; + xfree(w); + /* compute residual vector r = A*D*A'*y - h */ + r = xcalloc(1+m, sizeof(double)); + /* w := A'*y */ + w = xcalloc(1+n, sizeof(double)); + AT_by_vec(csa, y, w); + /* w := D*w */ + for (j = 1; j <= n; j++) w[j] *= csa->D[j]; + /* r := A*w */ + A_by_vec(csa, w, r); + xfree(w); + /* r := r - h */ + for (i = 1; i <= m; i++) r[i] -= h[i]; + /* check for numeric stability */ + for (i = 1; i <= m; i++) + { if (fabs(r[i]) / (1.0 + fabs(h[i])) > 1e-4) + { ret = 1; + break; + } + } + xfree(h); + xfree(r); + return ret; +} + +/*********************************************************************** +* solve_NS - solve Newtonian system +* +* This routine solves the Newtonian system: +* +* A*dx = p +* +* A'*dy + dz = q +* +* Z*dx + X*dz = r +* +* where X = diag(x[j]), Z = diag(z[j]), by reducing it to the normal +* equation system: +* +* (A*inv(Z)*X*A')*dy = A*inv(Z)*(X*q-r)+p +* +* (it is assumed that the matrix A*inv(Z)*X*A' has been factorized by +* the routine decomp_NE). +* +* Once vector dy has been computed the routine computes vectors dx and +* dz as follows: +* +* dx = inv(Z)*(X*(A'*dy-q)+r) +* +* dz = inv(X)*(r-Z*dx) +* +* The routine solve_NS returns the same code which was reported by the +* routine solve_NE (see above). */ + +static int solve_NS(struct csa *csa, double p[], double q[], double r[], + double dx[], double dy[], double dz[]) +{ int m = csa->m; + int n = csa->n; + double *x = csa->x; + double *z = csa->z; + int i, j, ret; + double *w = dx; + /* compute the vector of right-hand sides A*inv(Z)*(X*q-r)+p for + the normal equation system */ + for (j = 1; j <= n; j++) + w[j] = (x[j] * q[j] - r[j]) / z[j]; + A_by_vec(csa, w, dy); + for (i = 1; i <= m; i++) dy[i] += p[i]; + /* solve the normal equation system to compute vector dy */ + ret = solve_NE(csa, dy); + /* compute vectors dx and dz */ + AT_by_vec(csa, dy, dx); + for (j = 1; j <= n; j++) + { dx[j] = (x[j] * (dx[j] - q[j]) + r[j]) / z[j]; + dz[j] = (r[j] - z[j] * dx[j]) / x[j]; + } + return ret; +} + +/*********************************************************************** +* initial_point - choose initial point using Mehrotra's heuristic +* +* This routine chooses a starting point using a heuristic proposed in +* the paper: +* +* S. Mehrotra. On the implementation of a primal-dual interior point +* method. SIAM J. on Optim., 2(4), pp. 575-601, 1992. +* +* The starting point x in the primal space is chosen as a solution of +* the following least squares problem: +* +* minimize ||x|| +* +* subject to A*x = b +* +* which can be computed explicitly as follows: +* +* x = A'*inv(A*A')*b +* +* Similarly, the starting point (y, z) in the dual space is chosen as +* a solution of the following least squares problem: +* +* minimize ||z|| +* +* subject to A'*y + z = c +* +* which can be computed explicitly as follows: +* +* y = inv(A*A')*A*c +* +* z = c - A'*y +* +* However, some components of the vectors x and z may be non-positive +* or close to zero, so the routine uses a Mehrotra's heuristic to find +* a more appropriate starting point. */ + +static void initial_point(struct csa *csa) +{ int m = csa->m; + int n = csa->n; + double *b = csa->b; + double *c = csa->c; + double *x = csa->x; + double *y = csa->y; + double *z = csa->z; + double *D = csa->D; + int i, j; + double dp, dd, ex, ez, xz; + /* factorize A*A' */ + for (j = 1; j <= n; j++) D[j] = 1.0; + decomp_NE(csa); + /* x~ = A'*inv(A*A')*b */ + for (i = 1; i <= m; i++) y[i] = b[i]; + solve_NE(csa, y); + AT_by_vec(csa, y, x); + /* y~ = inv(A*A')*A*c */ + A_by_vec(csa, c, y); + solve_NE(csa, y); + /* z~ = c - A'*y~ */ + AT_by_vec(csa, y,z); + for (j = 1; j <= n; j++) z[j] = c[j] - z[j]; + /* use Mehrotra's heuristic in order to choose more appropriate + starting point with positive components of vectors x and z */ + dp = dd = 0.0; + for (j = 1; j <= n; j++) + { if (dp < -1.5 * x[j]) dp = -1.5 * x[j]; + if (dd < -1.5 * z[j]) dd = -1.5 * z[j]; + } + /* note that b = 0 involves x = 0, and c = 0 involves y = 0 and + z = 0, so we need to be careful */ + if (dp == 0.0) dp = 1.5; + if (dd == 0.0) dd = 1.5; + ex = ez = xz = 0.0; + for (j = 1; j <= n; j++) + { ex += (x[j] + dp); + ez += (z[j] + dd); + xz += (x[j] + dp) * (z[j] + dd); + } + dp += 0.5 * (xz / ez); + dd += 0.5 * (xz / ex); + for (j = 1; j <= n; j++) + { x[j] += dp; + z[j] += dd; + xassert(x[j] > 0.0 && z[j] > 0.0); + } + return; +} + +/*********************************************************************** +* basic_info - perform basic computations at the current point +* +* This routine computes the following quantities at the current point: +* +* 1) value of the objective function: +* +* F = c'*x + c[0] +* +* 2) relative primal infeasibility: +* +* rpi = ||A*x-b|| / (1+||b||) +* +* 3) relative dual infeasibility: +* +* rdi = ||A'*y+z-c|| / (1+||c||) +* +* 4) primal-dual gap (relative difference between the primal and the +* dual objective function values): +* +* gap = |c'*x-b'*y| / (1+|c'*x|) +* +* 5) merit function: +* +* phi = ||A*x-b|| / max(1,||b||) + ||A'*y+z-c|| / max(1,||c||) + +* +* + |c'*x-b'*y| / max(1,||b||,||c||) +* +* 6) duality measure: +* +* mu = x'*z / n +* +* 7) the ratio of infeasibility to mu: +* +* rmu = max(||A*x-b||,||A'*y+z-c||) / mu +* +* where ||*|| denotes euclidian norm, *' denotes transposition. */ + +static void basic_info(struct csa *csa) +{ int m = csa->m; + int n = csa->n; + double *b = csa->b; + double *c = csa->c; + double *x = csa->x; + double *y = csa->y; + double *z = csa->z; + int i, j; + double norm1, bnorm, norm2, cnorm, cx, by, *work, temp; + /* compute value of the objective function */ + temp = c[0]; + for (j = 1; j <= n; j++) temp += c[j] * x[j]; + csa->obj = temp; + /* norm1 = ||A*x-b|| */ + work = xcalloc(1+m, sizeof(double)); + A_by_vec(csa, x, work); + norm1 = 0.0; + for (i = 1; i <= m; i++) + norm1 += (work[i] - b[i]) * (work[i] - b[i]); + norm1 = sqrt(norm1); + xfree(work); + /* bnorm = ||b|| */ + bnorm = 0.0; + for (i = 1; i <= m; i++) bnorm += b[i] * b[i]; + bnorm = sqrt(bnorm); + /* compute relative primal infeasibility */ + csa->rpi = norm1 / (1.0 + bnorm); + /* norm2 = ||A'*y+z-c|| */ + work = xcalloc(1+n, sizeof(double)); + AT_by_vec(csa, y, work); + norm2 = 0.0; + for (j = 1; j <= n; j++) + norm2 += (work[j] + z[j] - c[j]) * (work[j] + z[j] - c[j]); + norm2 = sqrt(norm2); + xfree(work); + /* cnorm = ||c|| */ + cnorm = 0.0; + for (j = 1; j <= n; j++) cnorm += c[j] * c[j]; + cnorm = sqrt(cnorm); + /* compute relative dual infeasibility */ + csa->rdi = norm2 / (1.0 + cnorm); + /* by = b'*y */ + by = 0.0; + for (i = 1; i <= m; i++) by += b[i] * y[i]; + /* cx = c'*x */ + cx = 0.0; + for (j = 1; j <= n; j++) cx += c[j] * x[j]; + /* compute primal-dual gap */ + csa->gap = fabs(cx - by) / (1.0 + fabs(cx)); + /* compute merit function */ + csa->phi = 0.0; + csa->phi += norm1 / (bnorm > 1.0 ? bnorm : 1.0); + csa->phi += norm2 / (cnorm > 1.0 ? cnorm : 1.0); + temp = 1.0; + if (temp < bnorm) temp = bnorm; + if (temp < cnorm) temp = cnorm; + csa->phi += fabs(cx - by) / temp; + /* compute duality measure */ + temp = 0.0; + for (j = 1; j <= n; j++) temp += x[j] * z[j]; + csa->mu = temp / (double)n; + /* compute the ratio of infeasibility to mu */ + csa->rmu = (norm1 > norm2 ? norm1 : norm2) / csa->mu; + return; +} + +/*********************************************************************** +* make_step - compute next point using Mehrotra's technique +* +* This routine computes the next point using the predictor-corrector +* technique proposed in the paper: +* +* S. Mehrotra. On the implementation of a primal-dual interior point +* method. SIAM J. on Optim., 2(4), pp. 575-601, 1992. +* +* At first, the routine computes so called affine scaling (predictor) +* direction (dx_aff,dy_aff,dz_aff) which is a solution of the system: +* +* A*dx_aff = b - A*x +* +* A'*dy_aff + dz_aff = c - A'*y - z +* +* Z*dx_aff + X*dz_aff = - X*Z*e +* +* where (x,y,z) is the current point, X = diag(x[j]), Z = diag(z[j]), +* e = (1,...,1)'. +* +* Then, the routine computes the centering parameter sigma, using the +* following Mehrotra's heuristic: +* +* alfa_aff_p = inf{0 <= alfa <= 1 | x+alfa*dx_aff >= 0} +* +* alfa_aff_d = inf{0 <= alfa <= 1 | z+alfa*dz_aff >= 0} +* +* mu_aff = (x+alfa_aff_p*dx_aff)'*(z+alfa_aff_d*dz_aff)/n +* +* sigma = (mu_aff/mu)^3 +* +* where alfa_aff_p is the maximal stepsize along the affine scaling +* direction in the primal space, alfa_aff_d is the maximal stepsize +* along the same direction in the dual space. +* +* After determining sigma the routine computes so called centering +* (corrector) direction (dx_cc,dy_cc,dz_cc) which is the solution of +* the system: +* +* A*dx_cc = 0 +* +* A'*dy_cc + dz_cc = 0 +* +* Z*dx_cc + X*dz_cc = sigma*mu*e - X*Z*e +* +* Finally, the routine computes the combined direction +* +* (dx,dy,dz) = (dx_aff,dy_aff,dz_aff) + (dx_cc,dy_cc,dz_cc) +* +* and determines maximal primal and dual stepsizes along the combined +* direction: +* +* alfa_max_p = inf{0 <= alfa <= 1 | x+alfa*dx >= 0} +* +* alfa_max_d = inf{0 <= alfa <= 1 | z+alfa*dz >= 0} +* +* In order to prevent the next point to be too close to the boundary +* of the positive ortant, the routine decreases maximal stepsizes: +* +* alfa_p = gamma_p * alfa_max_p +* +* alfa_d = gamma_d * alfa_max_d +* +* where gamma_p and gamma_d are scaling factors, and computes the next +* point: +* +* x_new = x + alfa_p * dx +* +* y_new = y + alfa_d * dy +* +* z_new = z + alfa_d * dz +* +* which becomes the current point on the next iteration. */ + +static int make_step(struct csa *csa) +{ int m = csa->m; + int n = csa->n; + double *b = csa->b; + double *c = csa->c; + double *x = csa->x; + double *y = csa->y; + double *z = csa->z; + double *dx_aff = csa->dx_aff; + double *dy_aff = csa->dy_aff; + double *dz_aff = csa->dz_aff; + double *dx_cc = csa->dx_cc; + double *dy_cc = csa->dy_cc; + double *dz_cc = csa->dz_cc; + double *dx = csa->dx; + double *dy = csa->dy; + double *dz = csa->dz; + int i, j, ret = 0; + double temp, gamma_p, gamma_d, *p, *q, *r; + /* allocate working arrays */ + p = xcalloc(1+m, sizeof(double)); + q = xcalloc(1+n, sizeof(double)); + r = xcalloc(1+n, sizeof(double)); + /* p = b - A*x */ + A_by_vec(csa, x, p); + for (i = 1; i <= m; i++) p[i] = b[i] - p[i]; + /* q = c - A'*y - z */ + AT_by_vec(csa, y,q); + for (j = 1; j <= n; j++) q[j] = c[j] - q[j] - z[j]; + /* r = - X * Z * e */ + for (j = 1; j <= n; j++) r[j] = - x[j] * z[j]; + /* solve the first Newtonian system */ + if (solve_NS(csa, p, q, r, dx_aff, dy_aff, dz_aff)) + { ret = 1; + goto done; + } + /* alfa_aff_p = inf{0 <= alfa <= 1 | x + alfa*dx_aff >= 0} */ + /* alfa_aff_d = inf{0 <= alfa <= 1 | z + alfa*dz_aff >= 0} */ + csa->alfa_aff_p = csa->alfa_aff_d = 1.0; + for (j = 1; j <= n; j++) + { if (dx_aff[j] < 0.0) + { temp = - x[j] / dx_aff[j]; + if (csa->alfa_aff_p > temp) csa->alfa_aff_p = temp; + } + if (dz_aff[j] < 0.0) + { temp = - z[j] / dz_aff[j]; + if (csa->alfa_aff_d > temp) csa->alfa_aff_d = temp; + } + } + /* mu_aff = (x+alfa_aff_p*dx_aff)' * (z+alfa_aff_d*dz_aff) / n */ + temp = 0.0; + for (j = 1; j <= n; j++) + temp += (x[j] + csa->alfa_aff_p * dx_aff[j]) * + (z[j] + csa->alfa_aff_d * dz_aff[j]); + csa->mu_aff = temp / (double)n; + /* sigma = (mu_aff/mu)^3 */ + temp = csa->mu_aff / csa->mu; + csa->sigma = temp * temp * temp; + /* p = 0 */ + for (i = 1; i <= m; i++) p[i] = 0.0; + /* q = 0 */ + for (j = 1; j <= n; j++) q[j] = 0.0; + /* r = sigma * mu * e - X * Z * e */ + for (j = 1; j <= n; j++) + r[j] = csa->sigma * csa->mu - dx_aff[j] * dz_aff[j]; + /* solve the second Newtonian system with the same coefficients + but with altered right-hand sides */ + if (solve_NS(csa, p, q, r, dx_cc, dy_cc, dz_cc)) + { ret = 1; + goto done; + } + /* (dx,dy,dz) = (dx_aff,dy_aff,dz_aff) + (dx_cc,dy_cc,dz_cc) */ + for (j = 1; j <= n; j++) dx[j] = dx_aff[j] + dx_cc[j]; + for (i = 1; i <= m; i++) dy[i] = dy_aff[i] + dy_cc[i]; + for (j = 1; j <= n; j++) dz[j] = dz_aff[j] + dz_cc[j]; + /* alfa_max_p = inf{0 <= alfa <= 1 | x + alfa*dx >= 0} */ + /* alfa_max_d = inf{0 <= alfa <= 1 | z + alfa*dz >= 0} */ + csa->alfa_max_p = csa->alfa_max_d = 1.0; + for (j = 1; j <= n; j++) + { if (dx[j] < 0.0) + { temp = - x[j] / dx[j]; + if (csa->alfa_max_p > temp) csa->alfa_max_p = temp; + } + if (dz[j] < 0.0) + { temp = - z[j] / dz[j]; + if (csa->alfa_max_d > temp) csa->alfa_max_d = temp; + } + } + /* determine scale factors (not implemented yet) */ + gamma_p = 0.90; + gamma_d = 0.90; + /* compute the next point */ + for (j = 1; j <= n; j++) + { x[j] += gamma_p * csa->alfa_max_p * dx[j]; + xassert(x[j] > 0.0); + } + for (i = 1; i <= m; i++) + y[i] += gamma_d * csa->alfa_max_d * dy[i]; + for (j = 1; j <= n; j++) + { z[j] += gamma_d * csa->alfa_max_d * dz[j]; + xassert(z[j] > 0.0); + } +done: /* free working arrays */ + xfree(p); + xfree(q); + xfree(r); + return ret; +} + +/*********************************************************************** +* terminate - deallocate common storage area +* +* This routine frees all memory allocated to the common storage area +* used by interior-point method routines. */ + +static void terminate(struct csa *csa) +{ xfree(csa->D); + xfree(csa->P); + xfree(csa->S_ptr); + xfree(csa->S_ind); + xfree(csa->S_val); + xfree(csa->S_diag); + xfree(csa->U_ptr); + xfree(csa->U_ind); + xfree(csa->U_val); + xfree(csa->U_diag); + xfree(csa->phi_min); + xfree(csa->best_x); + xfree(csa->best_y); + xfree(csa->best_z); + xfree(csa->dx_aff); + xfree(csa->dy_aff); + xfree(csa->dz_aff); + xfree(csa->dx_cc); + xfree(csa->dy_cc); + xfree(csa->dz_cc); + return; +} + +/*********************************************************************** +* ipm_main - main interior-point method routine +* +* This is a main routine of the primal-dual interior-point method. +* +* The routine ipm_main returns one of the following codes: +* +* 0 - optimal solution found; +* 1 - problem has no feasible (primal or dual) solution; +* 2 - no convergence; +* 3 - iteration limit exceeded; +* 4 - numeric instability on solving Newtonian system. +* +* In case of non-zero return code the routine returns the best point, +* which has been reached during optimization. */ + +static int ipm_main(struct csa *csa) +{ int m = csa->m; + int n = csa->n; + int i, j, status; + double temp; + /* choose initial point using Mehrotra's heuristic */ + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Guessing initial point...\n"); + initial_point(csa); + /* main loop starts here */ + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Optimization begins...\n"); + for (;;) + { /* perform basic computations at the current point */ + basic_info(csa); + /* save initial value of rmu */ + if (csa->iter == 0) csa->rmu0 = csa->rmu; + /* accumulate values of min(phi[k]) and save the best point */ + xassert(csa->iter <= ITER_MAX); + if (csa->iter == 0 || csa->phi_min[csa->iter-1] > csa->phi) + { csa->phi_min[csa->iter] = csa->phi; + csa->best_iter = csa->iter; + for (j = 1; j <= n; j++) csa->best_x[j] = csa->x[j]; + for (i = 1; i <= m; i++) csa->best_y[i] = csa->y[i]; + for (j = 1; j <= n; j++) csa->best_z[j] = csa->z[j]; + csa->best_obj = csa->obj; + } + else + csa->phi_min[csa->iter] = csa->phi_min[csa->iter-1]; + /* display information at the current point */ + if (csa->parm->msg_lev >= GLP_MSG_ON) + xprintf("%3d: obj = %17.9e; rpi = %8.1e; rdi = %8.1e; gap =" + " %8.1e\n", csa->iter, csa->obj, csa->rpi, csa->rdi, + csa->gap); + /* check if the current point is optimal */ + if (csa->rpi < 1e-8 && csa->rdi < 1e-8 && csa->gap < 1e-8) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("OPTIMAL SOLUTION FOUND\n"); + status = 0; + break; + } + /* check if the problem has no feasible solution */ + temp = 1e5 * csa->phi_min[csa->iter]; + if (temp < 1e-8) temp = 1e-8; + if (csa->phi >= temp) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("PROBLEM HAS NO FEASIBLE PRIMAL/DUAL SOLUTION\n") + ; + status = 1; + break; + } + /* check for very slow convergence or divergence */ + if (((csa->rpi >= 1e-8 || csa->rdi >= 1e-8) && csa->rmu / + csa->rmu0 >= 1e6) || + (csa->iter >= 30 && csa->phi_min[csa->iter] >= 0.5 * + csa->phi_min[csa->iter - 30])) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("NO CONVERGENCE; SEARCH TERMINATED\n"); + status = 2; + break; + } + /* check for maximal number of iterations */ + if (csa->iter == ITER_MAX) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("ITERATION LIMIT EXCEEDED; SEARCH TERMINATED\n"); + status = 3; + break; + } + /* start the next iteration */ + csa->iter++; + /* factorize normal equation system */ + for (j = 1; j <= n; j++) csa->D[j] = csa->x[j] / csa->z[j]; + decomp_NE(csa); + /* compute the next point using Mehrotra's predictor-corrector + technique */ + if (make_step(csa)) + { if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("NUMERIC INSTABILITY; SEARCH TERMINATED\n"); + status = 4; + break; + } + } + /* restore the best point */ + if (status != 0) + { for (j = 1; j <= n; j++) csa->x[j] = csa->best_x[j]; + for (i = 1; i <= m; i++) csa->y[i] = csa->best_y[i]; + for (j = 1; j <= n; j++) csa->z[j] = csa->best_z[j]; + if (csa->parm->msg_lev >= GLP_MSG_ALL) + xprintf("Best point %17.9e was reached on iteration %d\n", + csa->best_obj, csa->best_iter); + } + /* return to the calling program */ + return status; +} + +/*********************************************************************** +* NAME +* +* ipm_solve - core LP solver based on the interior-point method +* +* SYNOPSIS +* +* #include "glpipm.h" +* int ipm_solve(glp_prob *P, const glp_iptcp *parm); +* +* DESCRIPTION +* +* The routine ipm_solve is a core LP solver based on the primal-dual +* interior-point method. +* +* The routine assumes the following standard formulation of LP problem +* to be solved: +* +* minimize +* +* F = c[0] + c[1]*x[1] + c[2]*x[2] + ... + c[n]*x[n] +* +* subject to linear constraints +* +* a[1,1]*x[1] + a[1,2]*x[2] + ... + a[1,n]*x[n] = b[1] +* +* a[2,1]*x[1] + a[2,2]*x[2] + ... + a[2,n]*x[n] = b[2] +* +* . . . . . . +* +* a[m,1]*x[1] + a[m,2]*x[2] + ... + a[m,n]*x[n] = b[m] +* +* and non-negative variables +* +* x[1] >= 0, x[2] >= 0, ..., x[n] >= 0 +* +* where: +* F is the objective function; +* x[1], ..., x[n] are (structural) variables; +* c[0] is a constant term of the objective function; +* c[1], ..., c[n] are objective coefficients; +* a[1,1], ..., a[m,n] are constraint coefficients; +* b[1], ..., b[n] are right-hand sides. +* +* The solution is three vectors x, y, and z, which are stored by the +* routine in the arrays x, y, and z, respectively. These vectors +* correspond to the best primal-dual point found during optimization. +* They are approximate solution of the following system (which is the +* Karush-Kuhn-Tucker optimality conditions): +* +* A*x = b (primal feasibility condition) +* +* A'*y + z = c (dual feasibility condition) +* +* x'*z = 0 (primal-dual complementarity condition) +* +* x >= 0, z >= 0 (non-negativity condition) +* +* where: +* x[1], ..., x[n] are primal (structural) variables; +* y[1], ..., y[m] are dual variables (Lagrange multipliers) for +* equality constraints; +* z[1], ..., z[n] are dual variables (Lagrange multipliers) for +* non-negativity constraints. +* +* RETURNS +* +* 0 LP has been successfully solved. +* +* GLP_ENOCVG +* No convergence. +* +* GLP_EITLIM +* Iteration limit exceeded. +* +* GLP_EINSTAB +* Numeric instability on solving Newtonian system. +* +* In case of non-zero return code the routine returns the best point, +* which has been reached during optimization. */ + +int ipm_solve(glp_prob *P, const glp_iptcp *parm) +{ struct csa _dsa, *csa = &_dsa; + int m = P->m; + int n = P->n; + int nnz = P->nnz; + GLPROW *row; + GLPCOL *col; + GLPAIJ *aij; + int i, j, loc, ret, *A_ind, *A_ptr; + double dir, *A_val, *b, *c, *x, *y, *z; + xassert(m > 0); + xassert(n > 0); + /* allocate working arrays */ + A_ptr = xcalloc(1+m+1, sizeof(int)); + A_ind = xcalloc(1+nnz, sizeof(int)); + A_val = xcalloc(1+nnz, sizeof(double)); + b = xcalloc(1+m, sizeof(double)); + c = xcalloc(1+n, sizeof(double)); + x = xcalloc(1+n, sizeof(double)); + y = xcalloc(1+m, sizeof(double)); + z = xcalloc(1+n, sizeof(double)); + /* prepare rows and constraint coefficients */ + loc = 1; + for (i = 1; i <= m; i++) + { row = P->row[i]; + xassert(row->type == GLP_FX); + b[i] = row->lb * row->rii; + A_ptr[i] = loc; + for (aij = row->ptr; aij != NULL; aij = aij->r_next) + { A_ind[loc] = aij->col->j; + A_val[loc] = row->rii * aij->val * aij->col->sjj; + loc++; + } + } + A_ptr[m+1] = loc; + xassert(loc-1 == nnz); + /* prepare columns and objective coefficients */ + if (P->dir == GLP_MIN) + dir = +1.0; + else if (P->dir == GLP_MAX) + dir = -1.0; + else + xassert(P != P); + c[0] = dir * P->c0; + for (j = 1; j <= n; j++) + { col = P->col[j]; + xassert(col->type == GLP_LO && col->lb == 0.0); + c[j] = dir * col->coef * col->sjj; + } + /* allocate and initialize the common storage area */ + csa->m = m; + csa->n = n; + csa->A_ptr = A_ptr; + csa->A_ind = A_ind; + csa->A_val = A_val; + csa->b = b; + csa->c = c; + csa->x = x; + csa->y = y; + csa->z = z; + csa->parm = parm; + initialize(csa); + /* solve LP with the interior-point method */ + ret = ipm_main(csa); + /* deallocate the common storage area */ + terminate(csa); + /* determine solution status */ + if (ret == 0) + { /* optimal solution found */ + P->ipt_stat = GLP_OPT; + ret = 0; + } + else if (ret == 1) + { /* problem has no feasible (primal or dual) solution */ + P->ipt_stat = GLP_NOFEAS; + ret = 0; + } + else if (ret == 2) + { /* no convergence */ + P->ipt_stat = GLP_INFEAS; + ret = GLP_ENOCVG; + } + else if (ret == 3) + { /* iteration limit exceeded */ + P->ipt_stat = GLP_INFEAS; + ret = GLP_EITLIM; + } + else if (ret == 4) + { /* numeric instability on solving Newtonian system */ + P->ipt_stat = GLP_INFEAS; + ret = GLP_EINSTAB; + } + else + xassert(ret != ret); + /* store row solution components */ + for (i = 1; i <= m; i++) + { row = P->row[i]; + row->pval = row->lb; + row->dval = dir * y[i] * row->rii; + } + /* store column solution components */ + P->ipt_obj = P->c0; + for (j = 1; j <= n; j++) + { col = P->col[j]; + col->pval = x[j] * col->sjj; + col->dval = dir * z[j] / col->sjj; + P->ipt_obj += col->coef * col->pval; + } + /* free working arrays */ + xfree(A_ptr); + xfree(A_ind); + xfree(A_val); + xfree(b); + xfree(c); + xfree(x); + xfree(y); + xfree(z); + return ret; +} + +/* eof */ |