/* Restriction bci = ai - bi + xi = Ti and z += ai*Ei + bi*Li */
void addBasicRestriction(glp_prob * Prob, int plane) {
int cardinal, constr[4], i = plane, cardRow;
double cValues[4];
char buf[AUXSIZE];
cardinal = glp_add_cols(Prob, 3);
sprintf(buf,"a%i",i);
glp_set_col_name(Prob, cardinal, buf);
glp_set_col_bnds(Prob, cardinal, GLP_LO, 0, 0);
glp_set_obj_coef(Prob, cardinal, planes[i].costE);
sprintf(buf,"b%i",i);
glp_set_col_name(Prob, cardinal+1, buf);
glp_set_col_bnds(Prob, cardinal+1, GLP_LO, 0, 0);
glp_set_obj_coef(Prob, cardinal+1, planes[i].costL);
sprintf(buf,"x%i",i);
glp_set_col_name(Prob, cardinal+2, buf);
if( planes[i].earliest == planes[i].latest )
glp_set_col_bnds(Prob, cardinal+2, GLP_FX, planes[i].earliest, 0);
else
glp_set_col_bnds(Prob, cardinal+2, GLP_DB, planes[i].earliest, planes[i].latest);
cardRow = glp_add_rows(Prob, 1);
sprintf(buf,"bc%i",i);
glp_set_row_name(Prob, cardRow, buf);
glp_set_row_bnds(Prob, cardRow, GLP_FX, planes[i].ideal, 0);
constr[3] = 1 + (constr[2] = 1 + (constr[1] = cardinal));
cValues[3] = cValues[1] = 1;
cValues[2] = -1;
glp_set_mat_row(Prob, cardRow, 3, constr, cValues);
}
void Add_obj()
{
int i;
char num[5];
sig = (float*)malloc(n * sizeof(float));
for(i = 0; i < n; i++)
sig[i] = 1.0;
stdev = sqrt(1.0 / (2*CODERATE*pow(10,SNR/10)));
for(i = 0; i < n; i++)
sig[i] += GeneGauss(0,stdev);
/////////////////////////////////////////////////////////
// assign the sig to cplex as the r
// here just print them
/* glpk_data = fopen("//home//why/document//lp//lp.txt","a+");
fprintf(glpk_data,"minimize f : %f*f1",sig[0]);
for(int i =1; i < n; i ++)
fprintf(glpk_data,"+%f*f%d" , sig[i],i+1);
fprintf(glpk_data,";\n");
fclose(glpk_data);
*/
/////////////////////////////////////////////////////////
glp_add_cols(lp, n);
glp_set_obj_dir(lp, GLP_MIN);
for(i = 1; i <= n; i ++)
{
sprintf(num, "%d", i);
glp_set_col_name(lp, i, num);
glp_set_col_bnds(lp, i, GLP_DB, 0.0, 1.0);
glp_set_obj_coef(lp, i, sig[i-1]);
}
free(sig);
}
int c_simplex_sparse(int m, int n, DMAT(c), DMAT(b), DVEC(s)) {
glp_prob *lp;
lp = glp_create_prob();
glp_set_obj_dir(lp, GLP_MAX);
int i,j,k;
int tot = cr - n;
glp_add_rows(lp, m);
glp_add_cols(lp, n);
//printf("%d %d\n",m,n);
// the first n values
for (k=1;k<=n;k++) {
glp_set_obj_coef(lp, k, AT(c, k-1, 2));
//printf("%d %f\n",k,AT(c, k-1, 2));
}
int * ia = malloc((1+tot)*sizeof(int));
int * ja = malloc((1+tot)*sizeof(int));
double * ar = malloc((1+tot)*sizeof(double));
for (k=1; k<= tot; k++) {
ia[k] = rint(AT(c,k-1+n,0));
ja[k] = rint(AT(c,k-1+n,1));
ar[k] = AT(c,k-1+n,2);
//printf("%d %d %f\n",ia[k],ja[k],ar[k]);
}
glp_load_matrix(lp, tot, ia, ja, ar);
int t;
for (i=1;i<=m;i++) {
switch((int)rint(AT(b,i-1,0))) {
case 0: { t = GLP_FR; break; }
case 1: { t = GLP_LO; break; }
case 2: { t = GLP_UP; break; }
case 3: { t = GLP_DB; break; }
default: { t = GLP_FX; break; }
}
glp_set_row_bnds(lp, i, t , AT(b,i-1,1), AT(b,i-1,2));
}
for (j=1;j<=n;j++) {
switch((int)rint(AT(b,m+j-1,0))) {
case 0: { t = GLP_FR; break; }
case 1: { t = GLP_LO; break; }
case 2: { t = GLP_UP; break; }
case 3: { t = GLP_DB; break; }
default: { t = GLP_FX; break; }
}
glp_set_col_bnds(lp, j, t , AT(b,m+j-1,1), AT(b,m+j-1,2));
}
glp_term_out(0);
glp_simplex(lp, NULL);
sp[0] = glp_get_status(lp);
sp[1] = glp_get_obj_val(lp);
for (k=1; k<=n; k++) {
sp[k+1] = glp_get_col_prim(lp, k);
}
glp_delete_prob(lp);
free(ia);
free(ja);
free(ar);
return 0;
}
/*
R is the random contraint data in row major memory layout
ridx is an N array of integers
soln is an array of length (n+1) soln[n] is t (objective value)
active_constr is an N-length 0-1 array
*/
void solve_lp(int N, int n, double* R, int* ridx, double* soln, int* active_constr)
{
double tol = 1.0e-14;
int size = (N+1)*(n+1) + 1; // We add one because GLPK indexes arrays
// starting at 1 instead of 0.
glp_prob *lp;
int* ia = malloc(size * sizeof(int));
int* ja = malloc(size * sizeof(int));
double* ar = malloc(size * sizeof(double));
int i, j;
lp = glp_create_prob();
glp_set_prob_name(lp, "portfolio");
glp_set_obj_dir(lp, GLP_MAX);
glp_add_rows(lp, N+1);
// Sampled constraints are ">= 0"
for (i = 1; i <= N; i++) {
glp_set_row_bnds(lp, i, GLP_LO, 0.0, 0.0);
}
// Sum = 1 constraint
glp_set_row_name(lp, N+1, "sum");
glp_set_row_bnds(lp, N+1, GLP_FX, 1.0, 1.0);
glp_add_cols(lp, n+1);
// Nonnegative variables y
for (i = 1; i <= n; i++) {
glp_set_col_bnds(lp, i, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, i, 0.0);
}
// Free variable t
glp_set_col_name(lp, n+1, "t");
glp_set_col_bnds(lp, n+1, GLP_FR, 0.0, 0.0);
glp_set_obj_coef(lp, n+1, 1.0);
// for (i = 0; i < N*(n-1); i++) {
// printf("%d: %g\n", i, R[i]);
// }
int idx = 1;
// Sampled constraints
for (i = 1; i <= N; i++) {
// Uncertain assets
for (j = 1; j < n; j++) {
ia[idx] = i;
ja[idx] = j;
ar[idx] = R[ ridx[(i-1)] * (n-1) + (j-1) ];
idx += 1;
}
// Fixed return asset
ia[idx] = i;
ja[idx] = n;
ar[idx] = 1.05;
idx += 1;
// t
ia[idx] = i;
ja[idx] = n+1;
ar[idx] = -1.0;
idx += 1;
}
// Sum = 1 constraint
for (i = 1; i <= n; i++) {
ia[idx] = N+1;
ja[idx] = i;
ar[idx] = 1.0;
idx += 1;
}
// t
ia[idx] = N+1;
ja[idx] = n+1;
ar[idx] = 0.0;
idx += 1;
// for (i = 1; i < size; i++) {
// printf("%d %d %g\n", ia[i], ja[i], ar[i]);
// }
glp_load_matrix(lp, size-1, ia, ja, ar);
// glp_scale_prob(lp, GLP_SF_AUTO);
glp_smcp param;
glp_init_smcp(¶m);
param.meth = GLP_PRIMAL;
//glp_std_basis(lp);
glp_simplex(lp, ¶m);
double z = glp_get_obj_val(lp);
// printf("z = %g\n", z);
if (soln) {
for (i = 0; i < n; i++) {
double y = glp_get_col_prim(lp, i+1);
soln[i] = y;
// printf("y%d = %g\n", i, y);
}
double t = glp_get_col_prim(lp, n+1);
soln[n] = t;
// printf("t = %g\n", glp_get_col_prim(lp, n+1));
}
for (i = 1; i <= N; i++) {
double slack = glp_get_row_prim(lp, i);
active_constr[i-1] = fabs(slack) < tol ? 1 : 0;
// printf("constr%d %d\n", i, active_constr[i-1]);
}
glp_delete_prob(lp);
// glp_free_env();
free(ia);
free(ja);
free(ar);
}
void lpx_set_obj_coef(glp_prob *lp, int j, double coef)
{ /* set (change) obj. coefficient or constant term */
glp_set_obj_coef(lp, j, coef);
return;
}
int max_flow_lp(int nn, int ne, const int beg[/*1+ne*/],
const int end[/*1+ne*/], const int cap[/*1+ne*/], int s, int t,
int x[/*1+ne*/])
{ glp_prob *lp;
glp_smcp smcp;
int i, k, nz, flow, *rn, *cn;
double temp, *aa;
/* create LP problem instance */
lp = glp_create_prob();
/* create LP rows; i-th row is the conservation condition of the
* flow at i-th node, i = 1, ..., nn */
glp_add_rows(lp, nn);
for (i = 1; i <= nn; i++)
glp_set_row_bnds(lp, i, GLP_FX, 0.0, 0.0);
/* create LP columns; k-th column is the elementary flow thru
* k-th edge, k = 1, ..., ne; the last column with the number
* ne+1 is the total flow through the network, which goes along
* a dummy feedback edge from the sink to the source */
glp_add_cols(lp, ne+1);
for (k = 1; k <= ne; k++)
{ xassert(cap[k] > 0);
glp_set_col_bnds(lp, k, GLP_DB, -cap[k], +cap[k]);
}
glp_set_col_bnds(lp, ne+1, GLP_FR, 0.0, 0.0);
/* build the constraint matrix; structurally this matrix is the
* incidence matrix of the network, so each its column (including
* the last column for the dummy edge) has exactly two non-zero
* entries */
rn = xalloc(1+2*(ne+1), sizeof(int));
cn = xalloc(1+2*(ne+1), sizeof(int));
aa = xalloc(1+2*(ne+1), sizeof(double));
nz = 0;
for (k = 1; k <= ne; k++)
{ /* x[k] > 0 means the elementary flow thru k-th edge goes from
* node beg[k] to node end[k] */
nz++, rn[nz] = beg[k], cn[nz] = k, aa[nz] = -1.0;
nz++, rn[nz] = end[k], cn[nz] = k, aa[nz] = +1.0;
}
/* total flow thru the network goes from the sink to the source
* along the dummy feedback edge */
nz++, rn[nz] = t, cn[nz] = ne+1, aa[nz] = -1.0;
nz++, rn[nz] = s, cn[nz] = ne+1, aa[nz] = +1.0;
/* check the number of non-zero entries */
xassert(nz == 2*(ne+1));
/* load the constraint matrix into the LP problem object */
glp_load_matrix(lp, nz, rn, cn, aa);
xfree(rn);
xfree(cn);
xfree(aa);
/* objective function is the total flow through the network to
* be maximized */
glp_set_obj_dir(lp, GLP_MAX);
glp_set_obj_coef(lp, ne + 1, 1.0);
/* solve LP instance with the (primal) simplex method */
glp_term_out(0);
glp_adv_basis(lp, 0);
glp_term_out(1);
glp_init_smcp(&smcp);
smcp.msg_lev = GLP_MSG_ON;
smcp.out_dly = 5000;
xassert(glp_simplex(lp, &smcp) == 0);
xassert(glp_get_status(lp) == GLP_OPT);
/* obtain optimal elementary flows thru edges of the network */
/* (note that the constraint matrix is unimodular and the data
* are integral, so all elementary flows in basic solution should
* also be integral) */
for (k = 1; k <= ne; k++)
{ temp = glp_get_col_prim(lp, k);
x[k] = (int)floor(temp + .5);
xassert(fabs(x[k] - temp) <= 1e-6);
}
/* obtain the maximum flow thru the original network which is the
* flow thru the dummy feedback edge */
temp = glp_get_col_prim(lp, ne+1);
flow = (int)floor(temp + .5);
xassert(fabs(flow - temp) <= 1e-6);
/* delete LP problem instance */
glp_delete_prob(lp);
/* return to the calling program */
return flow;
}
static bool Graph_solve(Graph& graph, size_t loops, PositionList* position_tbl) {
glp_prob* lp = glp_create_prob();
glp_set_prob_name(lp, "scaffold");
glp_set_obj_dir(lp, GLP_MIN);
size_t rows = 0, cols = 0, vals = 0;
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
++cols; // var x_i
for (Children::const_iterator j = i->second.children.begin(); j != i->second.children.end(); ++j) {
rows += 3;
cols += 2; // var e_i_j; var E_i_j;
vals += 7;
}
}
glp_add_rows(lp, rows);
glp_add_cols(lp, cols);
std::map< std::pair< size_t, size_t >, size_t > mapping;
{
size_t row = 1, col = 1;
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
mapping[std::make_pair(i->first, -1)] = col;
glp_set_col_bnds(lp, col++, GLP_LO, 0.0, 0.0); // x_i >= 0
for (Children::const_iterator j = i->second.children.begin(); j != i->second.children.end(); ++j) {
glp_set_row_bnds(lp, row++, GLP_FX, j->second, j->second);
glp_set_row_bnds(lp, row++, GLP_LO, 0.0, 0.0);
glp_set_row_bnds(lp, row++, GLP_LO, 0.0, 0.0);
mapping[std::make_pair(i->first, j->first)] = col;
++col; // var e_i
glp_set_obj_coef(lp, col++, 1.0);
}
}
}
LOG4CXX_TRACE(logger, boost::format(" rows = %d, cols = %d, vals = %d") % rows % cols % vals);
int* ia = new int[vals + 1];
int* ja = new int[vals + 1];
double* ra = new double[vals + 1];
{
size_t l = 1, row = 1, col = 1;
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
for (Children::const_iterator j = i->second.children.begin(); j != i->second.children.end(); ++j) {
// x_j - x_i + e_i_j = d_i_j
ia[l] = row;
ja[l] = mapping[std::make_pair(j->first, -1)];
ra[l] = 1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, -1)];
ra[l] = -1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 0;
ra[l] = 1.0;
++l;
++row;
// E_i_j + e_i_j >= 0
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 1;
ra[l] = 1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 0;
ra[l] = 1.0;
++l;
++row;
// E_i_j - e_i_j >= 0
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 1;
ra[l] = 1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 0;
ra[l] = -1.0;
++l;
++row;
}
}
}
glp_load_matrix(lp, vals, ia, ja, ra);
glp_smcp parm;
glp_init_smcp(&parm);
parm.it_lim = loops;
//parm.pricing = GLP_PT_PSE;
//parm.presolve = GLP_ON;
parm.msg_lev = GLP_MSG_ERR;
glp_simplex(lp, &parm);
double z = glp_get_obj_val(lp);
LOG4CXX_TRACE(logger, boost::format("z = %f") % z);
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
size_t val = glp_get_col_prim(lp, mapping[std::make_pair(i->first, -1)]);
if (position_tbl != NULL) {
(*position_tbl)[i->first] = val;
}
LOG4CXX_TRACE(logger, boost::format("x\t%d\t%d") % i->first % val);
}
delete[] ra;
delete[] ja;
delete[] ia;
glp_delete_prob(lp);
return true;
}
int main(int argc,char *argv[]){
int q[] = {atoi(argv[1]),atoi(argv[2]),atoi(argv[3])}; //queue length
int n[] = {atoi(argv[4]),atoi(argv[5]),atoi(argv[6])}; //different request number
float cmax = atof(argv[7]); //bandwidth limitation
glp_prob *mip = glp_create_prob();
glp_set_prob_name(mip, "sample");
glp_set_obj_dir(mip, GLP_MAX);
glp_add_rows(mip, 8);
glp_set_row_name(mip, 1, "n1");
if (n[0]==0)
glp_set_row_bnds(mip, 1, GLP_FX, 0.0, n[0]);
else
glp_set_row_bnds(mip, 1, GLP_DB, 0.0, n[0]);
glp_set_row_name(mip, 2, "n2");
if (n[1]==0)
glp_set_row_bnds(mip, 2, GLP_FX, 0.0, n[1]);
else
glp_set_row_bnds(mip, 2, GLP_DB, 0.0, n[1]);
glp_set_row_name(mip, 3, "n3");
if (n[2]==0)
glp_set_row_bnds(mip, 3, GLP_FX, 0.0, n[2]);
else
glp_set_row_bnds(mip, 3, GLP_DB, 0.0, n[2]);
glp_set_row_name(mip, 4, "c1");
glp_set_row_bnds(mip, 4, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 5, "c2");
glp_set_row_bnds(mip, 5, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 6, "c3");
glp_set_row_bnds(mip, 6, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 7, "c4");
glp_set_row_bnds(mip, 7, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 8, "c5");
glp_set_row_bnds(mip, 8, GLP_DB, 0.0, cmax);
glp_add_cols(mip, 15);
glp_set_col_name(mip, 1, "x11");
glp_set_col_bnds(mip, 1, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 1, q[0]); //queue length
glp_set_col_kind(mip, 1, GLP_IV);
glp_set_col_name(mip, 2, "x12");
glp_set_col_bnds(mip, 2, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 2, q[1]);
glp_set_col_kind(mip, 2, GLP_IV);
glp_set_col_name(mip, 3, "x13");
glp_set_col_bnds(mip, 3, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 3, q[2]);
glp_set_col_kind(mip, 3, GLP_IV);
glp_set_col_name(mip, 4, "x21");
glp_set_col_bnds(mip, 4, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 4, q[0]);
glp_set_col_kind(mip, 4, GLP_IV);
glp_set_col_name(mip, 5, "x22");
glp_set_col_bnds(mip, 5, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 5, q[1]);
glp_set_col_kind(mip, 5, GLP_IV);
glp_set_col_name(mip, 6, "x23");
glp_set_col_bnds(mip, 6, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 6, q[2]);
glp_set_col_kind(mip, 6, GLP_IV);
glp_set_col_name(mip, 7, "x31");
glp_set_col_bnds(mip, 7, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 7, q[0]);
glp_set_col_kind(mip, 7, GLP_IV);
glp_set_col_name(mip, 8, "x32");
glp_set_col_bnds(mip, 8, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 8, q[1]);
glp_set_col_kind(mip, 8, GLP_IV);
glp_set_col_name(mip, 9, "x33");
glp_set_col_bnds(mip, 9, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 9, q[2]);
glp_set_col_kind(mip, 9, GLP_IV);
glp_set_col_name(mip, 10, "x41");
glp_set_col_bnds(mip, 10, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 10, q[0]);
glp_set_col_kind(mip, 10, GLP_IV);
glp_set_col_name(mip, 11, "x42");
glp_set_col_bnds(mip, 11, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 11, q[1]);
glp_set_col_kind(mip, 11, GLP_IV);
glp_set_col_name(mip, 12, "x43");
glp_set_col_bnds(mip, 12, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 12, q[2]);
glp_set_col_kind(mip, 12, GLP_IV);
glp_set_col_name(mip, 13, "x51");
glp_set_col_bnds(mip, 13, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 13, q[0]);
glp_set_col_kind(mip, 13, GLP_IV);
glp_set_col_name(mip, 14, "x52");
glp_set_col_bnds(mip, 14, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 14, q[1]);
glp_set_col_kind(mip, 14, GLP_IV);
glp_set_col_name(mip, 15, "x53");
glp_set_col_bnds(mip, 15, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 15, q[2]);
glp_set_col_kind(mip, 15, GLP_IV);
int ia[1+30], ja[1+30];
double ar[1+30];
int i=1,j=1,k=1;
for (i=1;i<4;i++)
for (j=1;j<6;j++){
ia[k]=i,ja[k]=(j-1)*3+i,ar[k]=1;
k++;
}
ia[16]=4,ja[16]=1,ar[16]=10;
ia[17]=4,ja[17]=2,ar[17]=1;
ia[18]=4,ja[18]=3,ar[18]=0.1;
ia[19]=5,ja[19]=4,ar[19]=10;
ia[20]=5,ja[20]=5,ar[20]=1;
ia[21]=5,ja[21]=6,ar[21]=0.1;
ia[22]=6,ja[22]=7,ar[22]=10;
ia[23]=6,ja[23]=8,ar[23]=1;
ia[24]=6,ja[24]=9,ar[24]=0.1;
ia[25]=7,ja[25]=10,ar[25]=10;
ia[26]=7,ja[26]=11,ar[26]=1;
ia[27]=7,ja[27]=12,ar[27]=0.1;
ia[28]=8,ja[28]=13,ar[28]=10;
ia[29]=8,ja[29]=14,ar[29]=1;
ia[30]=8,ja[30]=15,ar[30]=0.1;
/*
for (i=1;i<31;i++){
printf("%d,%d,%f\n",ia[i],ja[i],ar[i]);
}
*/
glp_load_matrix(mip, 30, ia, ja, ar);
glp_iocp parm;
glp_init_iocp(&parm);
parm.presolve = GLP_ON;
int err = glp_intopt(mip, &parm);
//glp_simplex(mip, NULL);
// double t = glp_mip_obj_val(mip);
int result[15]={0};
for (i=0;i<15;i++){
result[i] = glp_mip_col_val(mip, i+1);
}
printf("\n");
//display the result
for (i=0;i<14;i++){
printf("%d,",result[i]);
}
printf("%d\n",result[14]);
glp_delete_prob(mip);
return 0;
}
void glp_mincost_lp(glp_prob *lp, glp_graph *G, int names, int v_rhs,
int a_low, int a_cap, int a_cost)
{ glp_vertex *v;
glp_arc *a;
int i, j, type, ind[1+2];
double rhs, low, cap, cost, val[1+2];
if (!(names == GLP_ON || names == GLP_OFF))
xerror("glp_mincost_lp: names = %d; invalid parameter\n",
names);
if (v_rhs >= 0 && v_rhs > G->v_size - (int)sizeof(double))
xerror("glp_mincost_lp: v_rhs = %d; invalid offset\n", v_rhs);
if (a_low >= 0 && a_low > G->a_size - (int)sizeof(double))
xerror("glp_mincost_lp: a_low = %d; invalid offset\n", a_low);
if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double))
xerror("glp_mincost_lp: a_cap = %d; invalid offset\n", a_cap);
if (a_cost >= 0 && a_cost > G->a_size - (int)sizeof(double))
xerror("glp_mincost_lp: a_cost = %d; invalid offset\n", a_cost)
;
glp_erase_prob(lp);
if (names) glp_set_prob_name(lp, G->name);
if (G->nv > 0) glp_add_rows(lp, G->nv);
for (i = 1; i <= G->nv; i++)
{ v = G->v[i];
if (names) glp_set_row_name(lp, i, v->name);
if (v_rhs >= 0)
memcpy(&rhs, (char *)v->data + v_rhs, sizeof(double));
else
rhs = 0.0;
glp_set_row_bnds(lp, i, GLP_FX, rhs, rhs);
}
if (G->na > 0) glp_add_cols(lp, G->na);
for (i = 1, j = 0; i <= G->nv; i++)
{ v = G->v[i];
for (a = v->out; a != NULL; a = a->t_next)
{ j++;
if (names)
{ char name[50+1];
sprintf(name, "x[%d,%d]", a->tail->i, a->head->i);
xassert(strlen(name) < sizeof(name));
glp_set_col_name(lp, j, name);
}
if (a->tail->i != a->head->i)
{ ind[1] = a->tail->i, val[1] = +1.0;
ind[2] = a->head->i, val[2] = -1.0;
glp_set_mat_col(lp, j, 2, ind, val);
}
if (a_low >= 0)
memcpy(&low, (char *)a->data + a_low, sizeof(double));
else
low = 0.0;
if (a_cap >= 0)
memcpy(&cap, (char *)a->data + a_cap, sizeof(double));
else
cap = 1.0;
if (cap == DBL_MAX)
type = GLP_LO;
else if (low != cap)
type = GLP_DB;
else
type = GLP_FX;
glp_set_col_bnds(lp, j, type, low, cap);
if (a_cost >= 0)
memcpy(&cost, (char *)a->data + a_cost, sizeof(double));
else
cost = 0.0;
glp_set_obj_coef(lp, j, cost);
}
}
xassert(j == G->na);
return;
}
int main(int argc, char** argv)
{
/*==================================================*/
/* Variables */
data d;
int* M;
/* GLPK */
int *ia, *ja;
double *ar;
double z;
double *x;
int notZeroCount;
/* Misc */
int i, pos;
/* Check up */
if(argc != 2)
{
printf("ERROR : no data file !\n");
exit(1);
}
/* Initialization */
filereader(argv[1], &d);
if(d.nbjour < 1)
{
printf("Obvious...\n");
return 0;
}
M = (int*) malloc ((d.nbjour +1)* sizeof(int));
M[d.nbjour] = d.d[d.nbjour];
for (i = d.nbjour-1; i >=0; --i)
{
M[i] = d.d[i] + M[i+1];
}
/* Problem creation*/
glp_prob *prob;
prob = glp_create_prob();
glp_set_prob_name(prob, "ULS");
glp_set_obj_dir(prob, GLP_MIN);
glp_smcp parm;
glp_init_smcp(&parm);
parm.msg_lev = GLP_MSG_OFF;
glp_iocp parmip;
glp_init_iocp(&parmip);
parmip.msg_lev = GLP_MSG_OFF;
/* Number of constraints : 2 * nbjour +2 */
glp_add_rows(prob, 2*d.nbjour +2);
for (i = 1; i <= d.nbjour; ++i)
{
glp_set_row_bnds(prob, i, GLP_FX, d.d[i], d.d[i]);
}
for (i = d.nbjour+1; i <= 2*d.nbjour; ++i)
{
glp_set_row_bnds(prob, i, GLP_LO, 0, 0);
}
glp_set_row_bnds(prob, 2*d.nbjour+1, GLP_FX, 0.0, 0.0);
glp_set_row_bnds(prob, 2*d.nbjour+2, GLP_FX, 0.0, 0.0);
/* Number of variables : 3*(nbjour +1)*/
glp_add_cols(prob, 3*(d.nbjour+1));
for (i = 0; i < d.nbjour +1; ++i)
{
glp_set_col_bnds(prob, i*3 +1, GLP_LO, 0.0, 0.0);
glp_set_col_bnds(prob, i*3 +2, GLP_LO, 0.0, 0.0);
glp_set_col_bnds(prob, i*3 +3, GLP_DB, 0.0, 1.0);
}
for (i = 1; i <= 3*(d.nbjour+1); ++i)
{
glp_set_col_kind(prob, i, GLP_CV);
}
/* Coefficients of the economic function */
glp_set_obj_coef(prob, 1, 0);
glp_set_obj_coef(prob, 2, 0);
glp_set_obj_coef(prob, 3, 0);
for (i = 1; i <=d.nbjour; ++i)
{
glp_set_obj_coef(prob, 3*i+1, d.p[i]);
glp_set_obj_coef(prob, 3*i+2, d.h[i]);
glp_set_obj_coef(prob, 3*i+3, d.f[i]);
}
/* Matrix */
notZeroCount = 5 * d.nbjour + 2;
ia = (int *) malloc ((1+notZeroCount) * sizeof(int));
ja = (int *) malloc ((1+notZeroCount) * sizeof(int));
ar = (double *) malloc ((1+notZeroCount) * sizeof(double));
pos = 1;
for (i = 1; i <= d.nbjour; ++i)
{
ia[pos] = i;
ia[pos+1] = i;
ia[pos+2] = i;
ja[pos] = i*3-1;
ja[pos+1] = i*3+1;
ja[pos+2] = i*3+2;
ar[pos] = 1.0;
ar[pos+1] = 1.0;
ar[pos+2] = -1.0;
pos += 3;
}
for (i = 1; i <= d.nbjour; ++i)
{
ia[pos] = i + d.nbjour;
ia[pos+1] = i + d.nbjour;
ja[pos] = i*3+1;
ja[pos+1] = i*3+3;
ar[pos] = -1.0;
ar[pos+1] = M[i];
pos += 2;
}
ia[pos] = 2*d.nbjour +1;
ia[pos+1] = 2 * d.nbjour+2;
ja[pos] = 3*(d.nbjour+1)-1;
ja[pos+1] = 2;
ar[pos] = 1.0;
ar[pos+1] = 1.0;
pos += 2;
glp_load_matrix(prob, notZeroCount, ia, ja , ar);
/* Writing in a file */
glp_write_lp(prob, NULL, "ULS.lp");
/* Branch and bound */
node* res = branchAndBound(prob);
displayNode(res);
}
void npp_build_prob(NPP *npp, glp_prob *prob)
{ /* build resultant (preprocessed) problem */
NPPROW *row;
NPPCOL *col;
NPPAIJ *aij;
int i, j, type, len, *ind;
double dir, *val;
glp_erase_prob(prob);
glp_set_prob_name(prob, npp->name);
glp_set_obj_name(prob, npp->obj);
glp_set_obj_dir(prob, npp->orig_dir);
if (npp->orig_dir == GLP_MIN)
dir = +1.0;
else if (npp->orig_dir == GLP_MAX)
dir = -1.0;
else
xassert(npp != npp);
glp_set_obj_coef(prob, 0, dir * npp->c0);
/* build rows */
for (row = npp->r_head; row != NULL; row = row->next)
{ row->temp = i = glp_add_rows(prob, 1);
glp_set_row_name(prob, i, row->name);
if (row->lb == -DBL_MAX && row->ub == +DBL_MAX)
type = GLP_FR;
else if (row->ub == +DBL_MAX)
type = GLP_LO;
else if (row->lb == -DBL_MAX)
type = GLP_UP;
else if (row->lb != row->ub)
type = GLP_DB;
else
type = GLP_FX;
glp_set_row_bnds(prob, i, type, row->lb, row->ub);
}
/* build columns and the constraint matrix */
ind = xcalloc(1+prob->m, sizeof(int));
val = xcalloc(1+prob->m, sizeof(double));
for (col = npp->c_head; col != NULL; col = col->next)
{ j = glp_add_cols(prob, 1);
glp_set_col_name(prob, j, col->name);
#if 0
glp_set_col_kind(prob, j, col->kind);
#else
glp_set_col_kind(prob, j, col->is_int ? GLP_IV : GLP_CV);
#endif
if (col->lb == -DBL_MAX && col->ub == +DBL_MAX)
type = GLP_FR;
else if (col->ub == +DBL_MAX)
type = GLP_LO;
else if (col->lb == -DBL_MAX)
type = GLP_UP;
else if (col->lb != col->ub)
type = GLP_DB;
else
type = GLP_FX;
glp_set_col_bnds(prob, j, type, col->lb, col->ub);
glp_set_obj_coef(prob, j, dir * col->coef);
len = 0;
for (aij = col->ptr; aij != NULL; aij = aij->c_next)
{ len++;
ind[len] = aij->row->temp;
val[len] = aij->val;
}
glp_set_mat_col(prob, j, len, ind, val);
}
xfree(ind);
xfree(val);
/* resultant problem has been built */
npp->m = prob->m;
npp->n = prob->n;
npp->nnz = prob->nnz;
npp->row_ref = xcalloc(1+npp->m, sizeof(int));
npp->col_ref = xcalloc(1+npp->n, sizeof(int));
for (row = npp->r_head, i = 0; row != NULL; row = row->next)
npp->row_ref[++i] = row->i;
for (col = npp->c_head, j = 0; col != NULL; col = col->next)
npp->col_ref[++j] = col->j;
/* transformed problem segment is no longer needed */
dmp_delete_pool(npp->pool), npp->pool = NULL;
npp->name = npp->obj = NULL;
npp->c0 = 0.0;
npp->r_head = npp->r_tail = NULL;
npp->c_head = npp->c_tail = NULL;
return;
}
void c_glp_set_obj_coef(glp_prob *lp, int j, double coef){
glp_set_obj_coef(lp, j, coef);
}
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
double *a, double *b, char *ctype, int *freeLB, double *lb,
int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
int save_pb, char *save_filename, char *filetype,
double *xmin, double *fmin, double *status,
double *lambda, double *redcosts, double *time, double *mem)
{
int typx = 0;
int method;
clock_t t_start = clock();
//Redirect standard output
if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
else glp_term_hook (NULL, NULL);
//-- Create an empty LP/MILP object
LPX *lp = lpx_create_prob ();
//-- Set the sense of optimization
if (sense == 1)
glp_set_obj_dir (lp, GLP_MIN);
else
glp_set_obj_dir (lp, GLP_MAX);
//-- Define the number of unknowns and their domains.
glp_add_cols (lp, n);
for (int i = 0; i < n; i++)
{
//-- Define type of the structural variables
if (! freeLB[i] && ! freeUB[i]) {
if ( lb[i] == ub[i] )
glp_set_col_bnds (lp, i+1, GLP_FX, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
}
else
{
if (! freeLB[i] && freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
else
{
if (freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
}
}
// -- Set the objective coefficient of the corresponding
// -- structural variable. No constant term is assumed.
glp_set_obj_coef(lp,i+1,c[i]);
if (isMIP)
glp_set_col_kind (lp, i+1, vartype[i]);
}
glp_add_rows (lp, m);
for (int i = 0; i < m; i++)
{
/* If the i-th row has no lower bound (types F,U), the
corrispondent parameter will be ignored.
If the i-th row has no upper bound (types F,L), the corrispondent
parameter will be ignored.
If the i-th row is of S type, the i-th LB is used, but
the i-th UB is ignored.
*/
switch (ctype[i])
{
case 'F': typx = GLP_FR; break;
// upper bound
case 'U': typx = GLP_UP; break;
// lower bound
case 'L': typx = GLP_LO; break;
// fixed constraint
case 'S': typx = GLP_FX; break;
// double-bounded variable
case 'D': typx = GLP_DB; break;
}
if ( typx == GLP_DB && -b[i] < b[i]) {
glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
}
else if(typx == GLP_DB && -b[i] == b[i]) {
glp_set_row_bnds (lp, i+1, GLP_FX, b[i], b[i]);
}
else {
// this should be glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
}
}
// Load constraint matrix A
glp_load_matrix (lp, nz, rn, cn, a);
// Save problem
if (save_pb) {
if (!strcmp(filetype,"cplex")){
if (glp_write_lp (lp, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"fixedmps")){
if (glp_write_mps (lp, GLP_MPS_DECK, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"freemps")){
if (glp_write_mps (lp, GLP_MPS_FILE, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{// plain text
if (lpx_print_prob (lp, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}
}
}
}
//-- scale the problem data (if required)
if (! glpIntParam[16] || lpsolver != 1) {
switch ( glpIntParam[1] ) {
case ( 0 ): glp_scale_prob( lp, GLP_SF_SKIP ); break;
case ( 1 ): glp_scale_prob( lp, GLP_SF_GM ); break;
case ( 2 ): glp_scale_prob( lp, GLP_SF_EQ ); break;
case ( 3 ): glp_scale_prob( lp, GLP_SF_AUTO ); break;
case ( 4 ): glp_scale_prob( lp, GLP_SF_2N ); break;
default :
mexErrMsgTxt("glpk: unrecognized scaling option");
longjmp (mark, -1);
}
}
else {
/* do nothing? or unscale?
glp_unscale_prob( lp );
*/
}
//-- build advanced initial basis (if required)
if (lpsolver == 1 && ! glpIntParam[16])
glp_adv_basis (lp, 0);
glp_smcp sParam;
glp_init_smcp(&sParam);
//-- set control parameters for simplex/exact method
if (lpsolver == 1 || lpsolver == 3){
//remap of control parameters for simplex method
sParam.msg_lev=glpIntParam[0]; // message level
// simplex method: primal/dual
switch ( glpIntParam[2] ) {
case 0: sParam.meth=GLP_PRIMAL; break;
case 1: sParam.meth=GLP_DUAL; break;
case 2: sParam.meth=GLP_DUALP; break;
default:
mexErrMsgTxt("glpk: unrecognized primal/dual method");
longjmp (mark, -1);
}
// pricing technique
if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
else sParam.pricing=GLP_PT_PSE;
// ratio test
if (glpIntParam[20]==0) sParam.r_test = GLP_RT_STD;
else sParam.r_test=GLP_RT_HAR;
//tollerances
sParam.tol_bnd=glpRealParam[1]; // primal feasible tollerance
sParam.tol_dj=glpRealParam[2]; // dual feasible tollerance
sParam.tol_piv=glpRealParam[3]; // pivot tollerance
sParam.obj_ll=glpRealParam[4]; // lower limit
sParam.obj_ul=glpRealParam[5]; // upper limit
// iteration limit
if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
else sParam.it_lim=glpIntParam[5];
// time limit
if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
else sParam.tm_lim=(int) glpRealParam[6];
sParam.out_frq=glpIntParam[7]; // output frequency
sParam.out_dly=(int) glpRealParam[7]; // output delay
// presolver
if (glpIntParam[16]) sParam.presolve=GLP_ON;
else sParam.presolve=GLP_OFF;
}else{
for(int i = 0; i < NIntP; i++) {
// skip assinging ratio test or
if ( i == 18 || i == 20) continue;
lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
}
for (int i = 0; i < NRealP; i++) {
lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
}
}
//set MIP params if MIP....
glp_iocp iParam;
glp_init_iocp(&iParam);
if ( isMIP ){
method = 'I';
switch (glpIntParam[0]) { //message level
case 0: iParam.msg_lev = GLP_MSG_OFF; break;
case 1: iParam.msg_lev = GLP_MSG_ERR; break;
case 2: iParam.msg_lev = GLP_MSG_ON; break;
case 3: iParam.msg_lev = GLP_MSG_ALL; break;
default: mexErrMsgTxt("glpk: msg_lev bad param");
}
switch (glpIntParam[14]) { //branching param
case 0: iParam.br_tech = GLP_BR_FFV; break;
case 1: iParam.br_tech = GLP_BR_LFV; break;
case 2: iParam.br_tech = GLP_BR_MFV; break;
case 3: iParam.br_tech = GLP_BR_DTH; break;
default: mexErrMsgTxt("glpk: branch bad param");
}
switch (glpIntParam[15]) { //backtracking heuristic
case 0: iParam.bt_tech = GLP_BT_DFS; break;
case 1: iParam.bt_tech = GLP_BT_BFS; break;
case 2: iParam.bt_tech = GLP_BT_BLB; break;
case 3: iParam.bt_tech = GLP_BT_BPH; break;
default: mexErrMsgTxt("glpk: backtrack bad param");
}
if ( glpRealParam[8] > 0.0 && glpRealParam[8] < 1.0 )
iParam.tol_int = glpRealParam[8]; // absolute tolorence
else
mexErrMsgTxt("glpk: tolint must be between 0 and 1");
iParam.tol_obj = glpRealParam[9]; // relative tolarence
iParam.mip_gap = glpRealParam[10]; // realative gap tolerance
// set time limit for mip
if ( glpRealParam[6] < 0.0 || glpRealParam[6] > 1e6 )
iParam.tm_lim = INT_MAX;
else
iParam.tm_lim = (int)(1000.0 * glpRealParam[6] );
// Choose Cutsets for mip
// shut all cuts off, then start over....
iParam.gmi_cuts = GLP_OFF;
iParam.mir_cuts = GLP_OFF;
iParam.cov_cuts = GLP_OFF;
iParam.clq_cuts = GLP_OFF;
switch( glpIntParam[17] ) {
case 0: break;
case 1: iParam.gmi_cuts = GLP_ON; break;
case 2: iParam.mir_cuts = GLP_ON; break;
case 3: iParam.cov_cuts = GLP_ON; break;
case 4: iParam.clq_cuts = GLP_ON; break;
case 5: iParam.clq_cuts = GLP_ON;
iParam.gmi_cuts = GLP_ON;
iParam.mir_cuts = GLP_ON;
iParam.cov_cuts = GLP_ON;
iParam.clq_cuts = GLP_ON; break;
default: mexErrMsgTxt("glpk: cutset bad param");
}
switch( glpIntParam[18] ) { // pre-processing for mip
case 0: iParam.pp_tech = GLP_PP_NONE; break;
case 1: iParam.pp_tech = GLP_PP_ROOT; break;
case 2: iParam.pp_tech = GLP_PP_ALL; break;
default: mexErrMsgTxt("glpk: pprocess bad param");
}
if (glpIntParam[16]) iParam.presolve=GLP_ON;
else iParam.presolve=GLP_OFF;
if (glpIntParam[19]) iParam.binarize = GLP_ON;
else iParam.binarize = GLP_OFF;
}
else {
/* Choose simplex method ('S')
or interior point method ('T')
or Exact method ('E')
to solve the problem */
switch (lpsolver) {
case 1: method = 'S'; break;
case 2: method = 'T'; break;
case 3: method = 'E'; break;
default:
mexErrMsgTxt("glpk: lpsolver != lpsolver");
longjmp (mark, -1);
}
}
// now run the problem...
int errnum = 0;
switch (method) {
case 'I':
errnum = glp_intopt( lp, &iParam );
errnum += 200; //this is to avoid ambiguity in the return codes.
break;
case 'S':
errnum = glp_simplex(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
break;
case 'T':
errnum = glp_interior(lp, NULL );
errnum += 300; //this is to avoid ambiguity in the return codes.
break;
case 'E':
errnum = glp_exact(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
break;
default: /*xassert (method != method); */
mexErrMsgTxt("glpk: method != method");
longjmp (mark, -1);
}
if (errnum==100 || errnum==200 || errnum==300 || errnum==106 || errnum==107 || errnum==108 || errnum==109 || errnum==209 || errnum==214 || errnum==308) {
// Get status and object value
if (isMIP) {
*status = glp_mip_status (lp);
*fmin = glp_mip_obj_val (lp);
}
else {
if (lpsolver == 1 || lpsolver == 3) {
*status = glp_get_status (lp);
*fmin = glp_get_obj_val (lp);
}
else {
*status = glp_ipt_status (lp);
*fmin = glp_ipt_obj_val (lp);
}
}
// Get optimal solution (if exists)
if (isMIP) {
for (int i = 0; i < n; i++)
xmin[i] = glp_mip_col_val (lp, i+1);
}
else {
/* Primal values */
for (int i = 0; i < n; i++) {
if (lpsolver == 1 || lpsolver == 3)
xmin[i] = glp_get_col_prim (lp, i+1);
else
xmin[i] = glp_ipt_col_prim (lp, i+1);
}
/* Dual values */
for (int i = 0; i < m; i++) {
if (lpsolver == 1 || lpsolver == 3)
lambda[i] = glp_get_row_dual (lp, i+1);
else
lambda[i] = glp_ipt_row_dual (lp, i+1);
}
/* Reduced costs */
for (int i = 0; i < glp_get_num_cols (lp); i++) {
if (lpsolver == 1 || lpsolver == 3)
redcosts[i] = glp_get_col_dual (lp, i+1);
else
redcosts[i] = glp_ipt_col_dual (lp, i+1);
}
}
*time = (clock () - t_start) / CLOCKS_PER_SEC;
size_t tpeak;
glp_mem_usage(NULL, NULL, NULL, &tpeak);
*mem=((double) tpeak) / (1024);
lpx_delete_prob(lp);
return 0;
}
else {
// printf("errnum is %d\n", errnum);
}
lpx_delete_prob(lp);
/* this shouldn't be nessiary with glp_deleted_prob, but try it
if we have weird behavior again... */
glp_free_env();
*status = errnum;
return errnum;
}
static PyObject *integer(PyObject *self, PyObject *args,
PyObject *kwrds)
{
matrix *c, *h, *b=NULL, *x=NULL;
PyObject *G, *A=NULL, *IntSet=NULL, *BinSet = NULL;
PyObject *t=NULL;
pyiocp *iocpParm = NULL;;
glp_iocp *options = NULL;
glp_prob *lp;
int m, n, p, i, j, k, nnz, nnzmax, *rn=NULL, *cn=NULL;
double *a=NULL, val;
char *kwlist[] = {"c", "G", "h", "A", "b", "I", "B","iocp", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OOO|OOOOO!", kwlist, &c,
&G, &h, &A, &b, &IntSet, &BinSet,iocp_t,&iocpParm)) return NULL;
if(!iocpParm)
{
iocpParm = (pyiocp*)malloc(sizeof(*iocpParm));
glp_init_iocp(&(iocpParm->obj));
}
if(iocpParm)
{
Py_INCREF(iocpParm);
options = &iocpParm->obj;
options->presolve = 1;
}
if ((Matrix_Check(G) && MAT_ID(G) != DOUBLE) ||
(SpMatrix_Check(G) && SP_ID(G) != DOUBLE) ||
(!Matrix_Check(G) && !SpMatrix_Check(G))){
PyErr_SetString(PyExc_TypeError, "G must be a 'd' matrix");
return NULL;
}
if ((m = Matrix_Check(G) ? MAT_NROWS(G) : SP_NROWS(G)) <= 0)
err_p_int("m");
if ((n = Matrix_Check(G) ? MAT_NCOLS(G) : SP_NCOLS(G)) <= 0)
err_p_int("n");
if (!Matrix_Check(h) || h->id != DOUBLE) err_dbl_mtrx("h");
if (h->nrows != m || h->ncols != 1){
PyErr_SetString(PyExc_ValueError, "incompatible dimensions");
return NULL;
}
if (A){
if ((Matrix_Check(A) && MAT_ID(A) != DOUBLE) ||
(SpMatrix_Check(A) && SP_ID(A) != DOUBLE) ||
(!Matrix_Check(A) && !SpMatrix_Check(A))){
PyErr_SetString(PyExc_ValueError, "A must be a dense "
"'d' matrix or a general sparse matrix");
return NULL;
}
if ((p = Matrix_Check(A) ? MAT_NROWS(A) : SP_NROWS(A)) < 0)
err_p_int("p");
if ((Matrix_Check(A) ? MAT_NCOLS(A) : SP_NCOLS(A)) != n){
PyErr_SetString(PyExc_ValueError, "incompatible "
"dimensions");
return NULL;
}
}
else p = 0;
if (b && (!Matrix_Check(b) || b->id != DOUBLE)) err_dbl_mtrx("b");
if ((b && (b->nrows != p || b->ncols != 1)) || (!b && p !=0 )){
PyErr_SetString(PyExc_ValueError, "incompatible dimensions");
return NULL;
}
if ((IntSet) && (!PyAnySet_Check(IntSet)))
PY_ERR_TYPE("invalid integer index set");
if ((BinSet) && (!PyAnySet_Check(BinSet)))
PY_ERR_TYPE("invalid binary index set");
lp = glp_create_prob();
glp_add_rows(lp, m+p);
glp_add_cols(lp, n);
for (i=0; i<n; i++){
glp_set_obj_coef(lp, i+1, MAT_BUFD(c)[i]);
glp_set_col_bnds(lp, i+1, GLP_FR, 0.0, 0.0);
}
for (i=0; i<m; i++)
glp_set_row_bnds(lp, i+1, GLP_UP, 0.0, MAT_BUFD(h)[i]);
for (i=0; i<p; i++)
glp_set_row_bnds(lp, i+m+1, GLP_FX, MAT_BUFD(b)[i],
MAT_BUFD(b)[i]);
nnzmax = (SpMatrix_Check(G) ? SP_NNZ(G) : m*n ) +
((A && SpMatrix_Check(A)) ? SP_NNZ(A) : p*n);
a = (double *) calloc(nnzmax+1, sizeof(double));
rn = (int *) calloc(nnzmax+1, sizeof(int));
cn = (int *) calloc(nnzmax+1, sizeof(int));
if (!a || !rn || !cn){
free(a); free(rn); free(cn); glp_delete_prob(lp);
return PyErr_NoMemory();
}
nnz = 0;
if (SpMatrix_Check(G)) {
for (j=0; j<n; j++) for (k=SP_COL(G)[j]; k<SP_COL(G)[j+1]; k++)
if ((val = SP_VALD(G)[k]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = SP_ROW(G)[k]+1;
cn[1+nnz] = j+1;
nnz++;
}
}
else for (j=0; j<n; j++) for (i=0; i<m; i++)
if ((val = MAT_BUFD(G)[i+j*m]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = i+1;
cn[1+nnz] = j+1;
nnz++;
}
if (A && SpMatrix_Check(A)){
for (j=0; j<n; j++) for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1]; k++)
if ((val = SP_VALD(A)[k]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = m+SP_ROW(A)[k]+1;
cn[1+nnz] = j+1;
nnz++;
}
}
else for (j=0; j<n; j++) for (i=0; i<p; i++)
if ((val = MAT_BUFD(A)[i+j*p]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = m+i+1;
cn[1+nnz] = j+1;
nnz++;
}
glp_load_matrix(lp, nnz, rn, cn, a);
free(rn); free(cn); free(a);
if (!(t = PyTuple_New(2))) {
glp_delete_prob(lp);
return PyErr_NoMemory();
}
if (IntSet) {
PyObject *iter = PySequence_Fast(IntSet, "Critical error: not sequence");
for (i=0; i<PySet_GET_SIZE(IntSet); i++) {
PyObject *tmp = PySequence_Fast_GET_ITEM(iter, i);
#if PY_MAJOR_VERSION >= 3
if (!PyLong_Check(tmp)) {
#else
if (!PyInt_Check(tmp)) {
#endif
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR_TYPE("non-integer element in I");
}
#if PY_MAJOR_VERSION >= 3
int k = PyLong_AS_LONG(tmp);
#else
int k = PyInt_AS_LONG(tmp);
#endif
if ((k < 0) || (k >= n)) {
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR(PyExc_IndexError, "index element out of range in I");
}
glp_set_col_kind(lp, k+1, GLP_IV);
}
Py_DECREF(iter);
}
if (BinSet) {
PyObject *iter = PySequence_Fast(BinSet, "Critical error: not sequence");
for (i=0; i<PySet_GET_SIZE(BinSet); i++) {
PyObject *tmp = PySequence_Fast_GET_ITEM(iter, i);
#if PY_MAJOR_VERSION >= 3
if (!PyLong_Check(tmp)) {
#else
if (!PyInt_Check(tmp)) {
#endif
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR_TYPE("non-binary element in I");
}
#if PY_MAJOR_VERSION >= 3
int k = PyLong_AS_LONG(tmp);
#else
int k = PyInt_AS_LONG(tmp);
#endif
if ((k < 0) || (k >= n)) {
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR(PyExc_IndexError, "index element out of range in B");
}
glp_set_col_kind(lp, k+1, GLP_BV);
}
Py_DECREF(iter);
}
switch (glp_intopt(lp,options)){
case 0:
x = (matrix *) Matrix_New(n,1,DOUBLE);
if (!x) {
Py_XDECREF(iocpParm);
Py_XDECREF(t);
glp_delete_prob(lp);
return PyErr_NoMemory();
}
set_output_string(t,"optimal");
set_output_string(t,"optimal");
for (i=0; i<n; i++)
MAT_BUFD(x)[i] = glp_mip_col_val(lp, i+1);
PyTuple_SET_ITEM(t, 1, (PyObject *) x);
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
return (PyObject *) t;
case GLP_ETMLIM:
x = (matrix *) Matrix_New(n,1,DOUBLE);
if (!x) {
Py_XDECREF(t);
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
return PyErr_NoMemory();
}
set_output_string(t,"time limit exceeded");
for (i=0; i<n; i++)
MAT_BUFD(x)[i] = glp_mip_col_val(lp, i+1);
PyTuple_SET_ITEM(t, 1, (PyObject *) x);
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
return (PyObject *) t;
case GLP_EBOUND:
set_output_string(t,"incorrect bounds");
break;
case GLP_EFAIL:
set_output_string(t,"invalid MIP formulation");
break;
case GLP_ENOPFS:
set_output_string(t,"primal infeasible");
break;
case GLP_ENODFS:
set_output_string(t,"dual infeasible");
break;
case GLP_EMIPGAP:
set_output_string(t,"Relative mip gap tolerance reached");
break;
/*case LPX_E_ITLIM:
set_output_string(t,"maxiters exceeded");
break;*/
/*case LPX_E_SING:
set_output_string(t,"singular or ill-conditioned basis");
break;*/
default:
set_output_string(t,"unknown");
}
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
PyTuple_SET_ITEM(t, 1, Py_BuildValue(""));
return (PyObject *) t;
}
static PyMethodDef glpk_functions[] = {
{"lp", (PyCFunction) simplex, METH_VARARGS|METH_KEYWORDS,
doc_simplex},
{"ilp", (PyCFunction) integer, METH_VARARGS|METH_KEYWORDS,
doc_integer},
{NULL} /* Sentinel */
};
#if PY_MAJOR_VERSION >= 3
static PyModuleDef glpk_module_def = {
PyModuleDef_HEAD_INIT,
"glpk",
glpk__doc__,
-1,
glpk_functions,
NULL, NULL, NULL, NULL
};
void addglpkConstants (void)
{
PyModule_AddIntMacro(glpk_module, GLP_ON);
PyModule_AddIntMacro(glpk_module,GLP_OFF);
/* reason codes: */
PyModule_AddIntMacro(glpk_module,GLP_IROWGEN);
PyModule_AddIntMacro(glpk_module,GLP_IBINGO);
PyModule_AddIntMacro(glpk_module,GLP_IHEUR);
PyModule_AddIntMacro(glpk_module,GLP_ICUTGEN);
PyModule_AddIntMacro(glpk_module,GLP_IBRANCH);
PyModule_AddIntMacro(glpk_module,GLP_ISELECT);
PyModule_AddIntMacro(glpk_module,GLP_IPREPRO);
/* branch selection indicator: */
PyModule_AddIntMacro(glpk_module,GLP_NO_BRNCH);
PyModule_AddIntMacro(glpk_module,GLP_DN_BRNCH);
PyModule_AddIntMacro(glpk_module,GLP_UP_BRNCH);
/* return codes: */
PyModule_AddIntMacro(glpk_module,GLP_EBADB);
PyModule_AddIntMacro(glpk_module,GLP_ESING);
PyModule_AddIntMacro(glpk_module,GLP_ECOND);
PyModule_AddIntMacro(glpk_module,GLP_EBOUND);
PyModule_AddIntMacro(glpk_module,GLP_EFAIL);
PyModule_AddIntMacro(glpk_module,GLP_EOBJLL);
PyModule_AddIntMacro(glpk_module,GLP_EOBJUL);
PyModule_AddIntMacro(glpk_module,GLP_EITLIM);
PyModule_AddIntMacro(glpk_module,GLP_ETMLIM);
PyModule_AddIntMacro(glpk_module,GLP_ENOPFS);
PyModule_AddIntMacro(glpk_module,GLP_ENODFS);
PyModule_AddIntMacro(glpk_module,GLP_EROOT);
PyModule_AddIntMacro(glpk_module,GLP_ESTOP);
PyModule_AddIntMacro(glpk_module,GLP_EMIPGAP);
PyModule_AddIntMacro(glpk_module,GLP_ENOFEAS);
PyModule_AddIntMacro(glpk_module,GLP_ENOCVG);
PyModule_AddIntMacro(glpk_module,GLP_EINSTAB);
PyModule_AddIntMacro(glpk_module,GLP_EDATA);
PyModule_AddIntMacro(glpk_module,GLP_ERANGE);
/* condition indicator: */
PyModule_AddIntMacro(glpk_module,GLP_KKT_PE);
PyModule_AddIntMacro(glpk_module,GLP_KKT_PB);
PyModule_AddIntMacro(glpk_module,GLP_KKT_DE);
PyModule_AddIntMacro(glpk_module,GLP_KKT_DB);
PyModule_AddIntMacro(glpk_module,GLP_KKT_CS);
/* MPS file format: */
PyModule_AddIntMacro(glpk_module,GLP_MPS_DECK);
PyModule_AddIntMacro(glpk_module,GLP_MPS_FILE);
/* simplex method control parameters */
/* message level: */
PyModule_AddIntMacro(glpk_module,GLP_MSG_OFF);
PyModule_AddIntMacro(glpk_module,GLP_MSG_ERR);
PyModule_AddIntMacro(glpk_module,GLP_MSG_ON);
PyModule_AddIntMacro(glpk_module,GLP_MSG_ALL);
PyModule_AddIntMacro(glpk_module,GLP_MSG_DBG);
/* simplex method option: */
PyModule_AddIntMacro(glpk_module,GLP_PRIMAL);
PyModule_AddIntMacro(glpk_module,GLP_DUALP);
PyModule_AddIntMacro(glpk_module,GLP_DUAL);
/* pricing technique: */
PyModule_AddIntMacro(glpk_module,GLP_PT_STD);
PyModule_AddIntMacro(glpk_module,GLP_PT_PSE);
/* ratio test technique: */
PyModule_AddIntMacro(glpk_module,GLP_RT_STD);
PyModule_AddIntMacro(glpk_module,GLP_RT_HAR);
/* interior-point solver control parameters */
/* ordering algorithm: */
PyModule_AddIntMacro(glpk_module,GLP_ORD_NONE);
PyModule_AddIntMacro(glpk_module,GLP_ORD_QMD);
PyModule_AddIntMacro(glpk_module,GLP_ORD_AMD);
PyModule_AddIntMacro(glpk_module,GLP_ORD_SYMAMD);
}
PyMODINIT_FUNC PyInit_glpk(void)
{
if (!(glpk_module = PyModule_Create(&glpk_module_def))) return NULL;
if (PyType_Ready(&iocp_t) < 0 || (PyType_Ready(&smcp_t) < 0)) return NULL;
/* Adding macros */
addglpkConstants();
/* Adding option lists as objects */
Py_INCREF(&smcp_t);
PyModule_AddObject(glpk_module,"smcp",(PyObject*)&smcp_t);
Py_INCREF(&iocp_t);
PyModule_AddObject(glpk_module,"iocp",(PyObject*)&iocp_t);
if (import_cvxopt() < 0) return NULL;
return glpk_module;
}
#else
PyMODINIT_FUNC initglpk(void)
{
glpk_module = Py_InitModule3("cvxopt.glpk", glpk_functions,
glpk__doc__);
if (PyType_Ready(&iocp_t) < 0 || (PyType_Ready(&smcp_t) < 0)) return NULL;
addglpkConstants();
Py_INCREF(&smcp_t);
PyModule_AddObject(glpk_module,"smcp",(PyObject*)&smcp_t);
Py_INCREF(&iocp_t);
PyModule_AddObject(glpk_module,"iocp",(PyObject*)&iocp_t);
if (import_cvxopt() < 0) return;
}
static PyObject *simplex(PyObject *self, PyObject *args,
PyObject *kwrds)
{
matrix *c, *h, *b=NULL, *x=NULL, *z=NULL, *y=NULL;
PyObject *G, *A=NULL, *t=NULL;
glp_prob *lp;
glp_smcp *options = NULL;
pysmcp *smcpParm = NULL;
int m, n, p, i, j, k, nnz, nnzmax, *rn=NULL, *cn=NULL;
double *a=NULL, val;
char *kwlist[] = {"c", "G", "h", "A", "b","options", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OOO|OOO!", kwlist, &c,
&G, &h, &A, &b,&smcp_t,&smcpParm)) return NULL;
if ((Matrix_Check(G) && MAT_ID(G) != DOUBLE) ||
(SpMatrix_Check(G) && SP_ID(G) != DOUBLE) ||
(!Matrix_Check(G) && !SpMatrix_Check(G))){
PyErr_SetString(PyExc_TypeError, "G must be a 'd' matrix");
return NULL;
}
if ((m = Matrix_Check(G) ? MAT_NROWS(G) : SP_NROWS(G)) <= 0)
err_p_int("m");
if ((n = Matrix_Check(G) ? MAT_NCOLS(G) : SP_NCOLS(G)) <= 0)
err_p_int("n");
if (!Matrix_Check(h) || h->id != DOUBLE) err_dbl_mtrx("h");
if (h->nrows != m || h->ncols != 1){
PyErr_SetString(PyExc_ValueError, "incompatible dimensions");
return NULL;
}
if (A){
if ((Matrix_Check(A) && MAT_ID(A) != DOUBLE) ||
(SpMatrix_Check(A) && SP_ID(A) != DOUBLE) ||
(!Matrix_Check(A) && !SpMatrix_Check(A))){
PyErr_SetString(PyExc_ValueError, "A must be a dense "
"'d' matrix or a general sparse matrix");
return NULL;
}
if ((p = Matrix_Check(A) ? MAT_NROWS(A) : SP_NROWS(A)) < 0)
err_p_int("p");
if ((Matrix_Check(A) ? MAT_NCOLS(A) : SP_NCOLS(A)) != n){
PyErr_SetString(PyExc_ValueError, "incompatible "
"dimensions");
return NULL;
}
}
else p = 0;
if (b && (!Matrix_Check(b) || b->id != DOUBLE)) err_dbl_mtrx("b");
if ((b && (b->nrows != p || b->ncols != 1)) || (!b && p !=0 )){
PyErr_SetString(PyExc_ValueError, "incompatible dimensions");
return NULL;
}
if(!smcpParm)
{
smcpParm = (pysmcp*)malloc(sizeof(*smcpParm));
glp_init_smcp(&(smcpParm->obj));
}
if(smcpParm)
{
Py_INCREF(smcpParm);
options = &smcpParm->obj;
options->presolve = 1;
}
lp = glp_create_prob();
glp_add_rows(lp, m+p);
glp_add_cols(lp, n);
for (i=0; i<n; i++){
glp_set_obj_coef(lp, i+1, MAT_BUFD(c)[i]);
glp_set_col_bnds(lp, i+1, GLP_FR, 0.0, 0.0);
}
for (i=0; i<m; i++)
glp_set_row_bnds(lp, i+1, GLP_UP, 0.0, MAT_BUFD(h)[i]);
for (i=0; i<p; i++)
glp_set_row_bnds(lp, i+m+1, GLP_FX, MAT_BUFD(b)[i],
MAT_BUFD(b)[i]);
nnzmax = (SpMatrix_Check(G) ? SP_NNZ(G) : m*n ) +
((A && SpMatrix_Check(A)) ? SP_NNZ(A) : p*n);
a = (double *) calloc(nnzmax+1, sizeof(double));
rn = (int *) calloc(nnzmax+1, sizeof(int));
cn = (int *) calloc(nnzmax+1, sizeof(int));
if (!a || !rn || !cn){
free(a); free(rn); free(cn); glp_delete_prob(lp);
return PyErr_NoMemory();
}
nnz = 0;
if (SpMatrix_Check(G)) {
for (j=0; j<n; j++) for (k=SP_COL(G)[j]; k<SP_COL(G)[j+1]; k++)
if ((val = SP_VALD(G)[k]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = SP_ROW(G)[k]+1;
cn[1+nnz] = j+1;
nnz++;
}
}
else for (j=0; j<n; j++) for (i=0; i<m; i++)
if ((val = MAT_BUFD(G)[i+j*m]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = i+1;
cn[1+nnz] = j+1;
nnz++;
}
if (A && SpMatrix_Check(A)){
for (j=0; j<n; j++) for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1]; k++)
if ((val = SP_VALD(A)[k]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = m+SP_ROW(A)[k]+1;
cn[1+nnz] = j+1;
nnz++;
}
}
else for (j=0; j<n; j++) for (i=0; i<p; i++)
if ((val = MAT_BUFD(A)[i+j*p]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = m+i+1;
cn[1+nnz] = j+1;
nnz++;
}
glp_load_matrix(lp, nnz, rn, cn, a);
free(rn); free(cn); free(a);
if (!(t = PyTuple_New(A ? 4 : 3))){
glp_delete_prob(lp);
return PyErr_NoMemory();
}
switch (glp_simplex(lp,options)){
case 0:
x = (matrix *) Matrix_New(n,1,DOUBLE);
z = (matrix *) Matrix_New(m,1,DOUBLE);
if (A) y = (matrix *) Matrix_New(p,1,DOUBLE);
if (!x || !z || (A && !y)){
Py_XDECREF(x);
Py_XDECREF(z);
Py_XDECREF(y);
Py_XDECREF(t);
Py_XDECREF(smcpParm);
glp_delete_prob(lp);
return PyErr_NoMemory();
}
set_output_string(t,"optimal");
for (i=0; i<n; i++)
MAT_BUFD(x)[i] = glp_get_col_prim(lp, i+1);
PyTuple_SET_ITEM(t, 1, (PyObject *) x);
for (i=0; i<m; i++)
MAT_BUFD(z)[i] = -glp_get_row_dual(lp, i+1);
PyTuple_SET_ITEM(t, 2, (PyObject *) z);
if (A){
for (i=0; i<p; i++)
MAT_BUFD(y)[i] = -glp_get_row_dual(lp, m+i+1);
PyTuple_SET_ITEM(t, 3, (PyObject *) y);
}
Py_XDECREF(smcpParm);
glp_delete_prob(lp);
return (PyObject *) t;
case GLP_EBADB:
set_output_string(t,"incorrect initial basis");
break;
case GLP_ESING:
set_output_string(t,"singular initial basis matrix");
break;
case GLP_ECOND:
set_output_string(t,"ill-conditioned initial basis matrix");
break;
case GLP_EBOUND:
set_output_string(t,"incorrect bounds");
break;
case GLP_EFAIL:
set_output_string(t,"solver failure");
break;
case GLP_EOBJLL:
set_output_string(t,"objective function reached lower limit");
break;
case GLP_EOBJUL:
set_output_string(t,"objective function reached upper limit");
break;
case GLP_EITLIM:
set_output_string(t,"iteration limit exceeded");
break;
case GLP_ETMLIM:
set_output_string(t,"time limit exceeded");
break;
case GLP_ENOPFS:
set_output_string(t,"primal infeasible");
break;
case GLP_ENODFS:
set_output_string(t,"dual infeasible");
break;
default:
set_output_string(t,"unknown");
break;
}
Py_XDECREF(smcpParm);
glp_delete_prob(lp);
PyTuple_SET_ITEM(t, 1, Py_BuildValue(""));
PyTuple_SET_ITEM(t, 2, Py_BuildValue(""));
if (A) PyTuple_SET_ITEM(t, 3, Py_BuildValue(""));
return (PyObject *) t;
}
void inline fence_insertert::mip_set_var(ilpt& ilp,
unsigned& i)
{
#ifdef HAVE_GLPK
glp_add_cols(ilp.lp, unique*fence_options);
//unsigned i=1;
for(; i<=unique*fence_options; i+=fence_options)
{
const bool has_cost = 1; //(po_plus.find(i)==po_plus.end());
/* has_cost == 0 => invisible variable */
assert(has_cost); // not useful for this problem
/* computes the sum of the frequencies of the cycles in which
this event appears, if requested */
float freq_sum = 0;
if(with_freq)
{
assert(instrumenter.set_of_cycles.size()==freq_table.size());
freq_sum += epsilon;
for(std::set<event_grapht::critical_cyclet>::const_iterator
C_j=instrumenter.set_of_cycles.begin();
C_j!=instrumenter.set_of_cycles.end();
++C_j)
{
/* filters */
if(filter_cycles(C_j->id)) continue;
/* if(C_j->find( col_to_var(i) )!=C_j->end()) */
std::list<unsigned>::const_iterator it;
for(it = C_j->begin(); it!=C_j->end() && col_to_var(i)!=*it; ++it);
if(it!=C_j->end())
freq_sum += freq_table[C_j->id];
}
}
else
freq_sum = 1;
if(model==Power || model==Unknown) {
/* dp variable for e */
const std::string name_dp="dp_"+i2string(i);
glp_set_col_name(ilp.lp, i, name_dp.c_str());
glp_set_col_bnds(ilp.lp, i, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(ilp.lp, i, (has_cost?fence_cost(Dp):0)*freq_sum);
glp_set_col_kind(ilp.lp, i, GLP_BV);
/* fence variable for e */
const std::string name_f="f_"+i2string(i);
glp_set_col_name(ilp.lp, i+1, name_f.c_str());
glp_set_col_bnds(ilp.lp, i+1, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(ilp.lp, i+1, (has_cost?fence_cost(Fence):0)*freq_sum);
glp_set_col_kind(ilp.lp, i+1, GLP_BV);
// Note: uncomment for br and cf fences
#if 0
/* br variable for e */
const std::string name_br="br_"+i2string(i);
glp_set_col_name(ilp.lp, i+2, name_br.c_str());
glp_set_col_bnds(ilp.lp, i+2, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(ilp.lp, i+2, (has_cost?fence_cost(Branching):0)*freq_sum);
glp_set_col_kind(ilp.lp, i+2, GLP_BV);
/* cf variable for e */
const std::string name_cf="cf_"+i2string(i);
glp_set_col_name(ilp.lp, i+3, name_cf.c_str());
glp_set_col_bnds(ilp.lp, i+3, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(ilp.lp, i+3, (has_cost?fence_cost(Ctlfence):0)*freq_sum);
glp_set_col_kind(ilp.lp, i+3, GLP_BV);
#endif
if(model==Power) {
/* lwf variable for e */
const std::string name_lwf="lwf_"+i2string(i);
glp_set_col_name(ilp.lp, i+2/*4*/, name_lwf.c_str());
glp_set_col_bnds(ilp.lp, i+2/*4*/, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(ilp.lp, i+2/*4*/,
(has_cost?fence_cost(Lwfence):0)*freq_sum);
glp_set_col_kind(ilp.lp, i+2/*4*/, GLP_BV);
}
}
else {
/* fence variable for e */
const std::string name_f="f_"+i2string(i);
glp_set_col_name(ilp.lp, i, name_f.c_str());
glp_set_col_bnds(ilp.lp, i, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(ilp.lp, i, (has_cost?fence_cost(Fence):0)*freq_sum);
glp_set_col_kind(ilp.lp, i, GLP_BV);
}
}
#else
throw "Sorry, musketeer requires glpk; please recompile\
musketeer with glpk.";
#endif
}
void glp_mpl_build_prob(glp_tran *tran, glp_prob *prob)
{ /* build LP/MIP problem instance from the model */
int m, n, i, j, t, kind, type, len, *ind;
double lb, ub, *val;
if (tran->phase != 3)
xerror("glp_mpl_build_prob: invalid call sequence\n");
/* erase the problem object */
glp_erase_prob(prob);
/* set problem name */
glp_set_prob_name(prob, mpl_get_prob_name(tran));
/* build rows (constraints) */
m = mpl_get_num_rows(tran);
if (m > 0)
glp_add_rows(prob, m);
for (i = 1; i <= m; i++)
{ /* set row name */
glp_set_row_name(prob, i, mpl_get_row_name(tran, i));
/* set row bounds */
type = mpl_get_row_bnds(tran, i, &lb, &ub);
switch (type)
{ case MPL_FR: type = GLP_FR; break;
case MPL_LO: type = GLP_LO; break;
case MPL_UP: type = GLP_UP; break;
case MPL_DB: type = GLP_DB; break;
case MPL_FX: type = GLP_FX; break;
default: xassert(type != type);
}
if (type == GLP_DB && fabs(lb - ub) < 1e-9 * (1.0 + fabs(lb)))
{ type = GLP_FX;
if (fabs(lb) <= fabs(ub)) ub = lb; else lb = ub;
}
glp_set_row_bnds(prob, i, type, lb, ub);
/* warn about non-zero constant term */
if (mpl_get_row_c0(tran, i) != 0.0)
xprintf("glp_mpl_build_prob: row %s; constant term %.12g ig"
"nored\n",
mpl_get_row_name(tran, i), mpl_get_row_c0(tran, i));
}
/* build columns (variables) */
n = mpl_get_num_cols(tran);
if (n > 0)
glp_add_cols(prob, n);
for (j = 1; j <= n; j++)
{ /* set column name */
glp_set_col_name(prob, j, mpl_get_col_name(tran, j));
/* set column kind */
kind = mpl_get_col_kind(tran, j);
switch (kind)
{ case MPL_NUM:
break;
case MPL_INT:
case MPL_BIN:
glp_set_col_kind(prob, j, GLP_IV);
break;
default:
xassert(kind != kind);
}
/* set column bounds */
type = mpl_get_col_bnds(tran, j, &lb, &ub);
switch (type)
{ case MPL_FR: type = GLP_FR; break;
case MPL_LO: type = GLP_LO; break;
case MPL_UP: type = GLP_UP; break;
case MPL_DB: type = GLP_DB; break;
case MPL_FX: type = GLP_FX; break;
default: xassert(type != type);
}
if (kind == MPL_BIN)
{ if (type == GLP_FR || type == GLP_UP || lb < 0.0) lb = 0.0;
if (type == GLP_FR || type == GLP_LO || ub > 1.0) ub = 1.0;
type = GLP_DB;
}
if (type == GLP_DB && fabs(lb - ub) < 1e-9 * (1.0 + fabs(lb)))
{ type = GLP_FX;
if (fabs(lb) <= fabs(ub)) ub = lb; else lb = ub;
}
glp_set_col_bnds(prob, j, type, lb, ub);
}
/* load the constraint matrix */
ind = xcalloc(1+n, sizeof(int));
val = xcalloc(1+n, sizeof(double));
for (i = 1; i <= m; i++)
{ len = mpl_get_mat_row(tran, i, ind, val);
glp_set_mat_row(prob, i, len, ind, val);
}
/* build objective function (the first objective is used) */
for (i = 1; i <= m; i++)
{ kind = mpl_get_row_kind(tran, i);
if (kind == MPL_MIN || kind == MPL_MAX)
{ /* set objective name */
glp_set_obj_name(prob, mpl_get_row_name(tran, i));
/* set optimization direction */
glp_set_obj_dir(prob, kind == MPL_MIN ? GLP_MIN : GLP_MAX);
/* set constant term */
glp_set_obj_coef(prob, 0, mpl_get_row_c0(tran, i));
/* set objective coefficients */
len = mpl_get_mat_row(tran, i, ind, val);
for (t = 1; t <= len; t++)
glp_set_obj_coef(prob, ind[t], val[t]);
break;
}
}
/* free working arrays */
xfree(ind);
xfree(val);
return;
}
/**
* Create the MLP problem
*
* @param mlp the MLP handle
* @param addresses the hashmap containing all adresses
* @return GNUNET_OK or GNUNET_SYSERR
*/
static int
mlp_create_problem (struct GAS_MLP_Handle *mlp, struct GNUNET_CONTAINER_MultiHashMap * addresses)
{
int res = GNUNET_OK;
int col;
int c;
char *name;
GNUNET_assert (mlp->prob == NULL);
/* create the glpk problem */
mlp->prob = glp_create_prob ();
/* Set a problem name */
glp_set_prob_name (mlp->prob, "gnunet ats bandwidth distribution");
/* Set optimization direction to maximize */
glp_set_obj_dir (mlp->prob, GLP_MAX);
/* Adding invariant columns */
/* Diversity d column */
col = glp_add_cols (mlp->prob, 1);
mlp->c_d = col;
/* Column name */
glp_set_col_name (mlp->prob, col, "d");
/* Column objective function coefficient */
glp_set_obj_coef (mlp->prob, col, mlp->co_D);
/* Column lower bound = 0.0 */
glp_set_col_bnds (mlp->prob, col, GLP_LO, 0.0, 0.0);
/* Utilization u column */
col = glp_add_cols (mlp->prob, 1);
mlp->c_u = col;
/* Column name */
glp_set_col_name (mlp->prob, col, "u");
/* Column objective function coefficient */
glp_set_obj_coef (mlp->prob, col, mlp->co_U);
/* Column lower bound = 0.0 */
glp_set_col_bnds (mlp->prob, col, GLP_LO, 0.0, 0.0);
#if ENABLE_C9
/* Relativity r column */
col = glp_add_cols (mlp->prob, 1);
mlp->c_r = col;
/* Column name */
glp_set_col_name (mlp->prob, col, "r");
/* Column objective function coefficient */
glp_set_obj_coef (mlp->prob, col, mlp->co_R);
/* Column lower bound = 0.0 */
glp_set_col_bnds (mlp->prob, col, GLP_LO, 0.0, 0.0);
#endif
/* Quality metric columns */
col = glp_add_cols(mlp->prob, mlp->m_q);
for (c = 0; c < mlp->m_q; c++)
{
mlp->c_q[c] = col + c;
GNUNET_asprintf (&name, "q_%u", mlp->q[c]);
glp_set_col_name (mlp->prob, col + c, name);
/* Column lower bound = 0.0 */
glp_set_col_bnds (mlp->prob, col + c, GLP_LO, 0.0, 0.0);
GNUNET_free (name);
/* Coefficient == Qm */
glp_set_obj_coef (mlp->prob, col + c, mlp->co_Q[c]);
}
/* Add columns for addresses */
GNUNET_CONTAINER_multihashmap_iterate (addresses, create_columns_it, mlp);
/* Add constraints */
mlp_add_constraints_all_addresses (mlp, addresses);
/* Load the matrix */
glp_load_matrix(mlp->prob, (mlp->ci-1), mlp->ia, mlp->ja, mlp->ar);
return res;
}
int CConstraints::GLPK_lp(CModel* pmodel)
{
glp_prob* lp = glp_create_prob();
int iRow = (int) m_vWeights.size();
//int iCol = (int) m_iWeightLength + m_iPatternNum;
int iSize = iRow * ( m_iWeightLength + 1);
int ia[10 + iSize], ja[1 + iSize];
double ar[1 + iSize];
glp_set_prob_name(lp, "StrLP");
glp_set_obj_dir(lp, GLP_MIN);
glp_add_rows(lp, (int) m_vWeights.size());
// setup rows
for (int i = 0; i < (int) m_vWeights.size(); i ++)
{
char tmp[200];
sprintf(tmp, "cc_%d", i + 1);
glp_set_row_name(lp, i + 1, tmp);
glp_set_row_bnds(lp, i + 1, GLP_LO, m_fDistance - m_fEpsilon, 0);
}
glp_add_cols(lp, m_iWeightLength + m_iPatternNum);
for (int i = 0; i < m_iWeightLength; i ++)
{
char tmp[200];
sprintf(tmp, "w%d", i + 1);
glp_set_col_name(lp, i + 1, tmp);
glp_set_col_bnds(lp, i + 1, GLP_LO, 0, 0.0);
glp_set_obj_coef(lp, i + 1, 1.0);
}
for (int i = 0; i < m_iPatternNum; i ++)
{
char tmp[200];
sprintf(tmp, "e%d", i + 1);
glp_set_col_name(lp, m_iWeightLength + i + 1, tmp);
glp_set_col_bnds(lp, m_iWeightLength + i + 1, GLP_LO, 0, 0.0);
glp_set_obj_coef(lp, m_iWeightLength + i + 1, m_fC / m_iPatternNum);
}
int iIndex = 1;
for (int i = 0; i < (int)m_vWeights.size(); i ++)
{
double* pd = m_vWeights[i];
for (int j = 0; j < (int) m_iWeightLength; j ++)
{
ia[iIndex] = i + 1, ja[iIndex] = j + 1;
if (pmodel->m_vSign[j] <= 0)
{
ar[iIndex] = -pd[j];
}
else
{
ar[iIndex] = pd[j];
}
iIndex ++;
}
ia[iIndex] = i + 1;
ja[iIndex] = m_iWeightLength + m_vPatternIndex[i] + 1;
//ar[iIndex] = 1;
ar[iIndex] = m_vLoss[i];
iIndex ++;
}
glp_load_matrix(lp, iIndex - 1, ia, ja, ar);
glp_simplex(lp, NULL);
double z = glp_get_obj_val(lp);
fprintf(stderr, "minimal value %f \n", z);
for (int i = 0; i < m_iWeightLength; i ++)
{
double x = glp_get_col_prim(lp, i + 1);
if (pmodel->m_vSign[i] <=0)
{
pmodel->m_vWeight[i] = -x;
}
else
{
pmodel->m_vWeight[i] = x;
}
if (x != 0) fprintf(stderr, "(w%d, %f)\t", i + 1, pmodel->m_vWeight[i]);
}
for (int i = 0; i < m_iPatternNum; i ++)
{
double x = glp_get_col_prim(lp, m_iWeightLength + i + 1);
pmodel->m_vTheta[i] = x;
if (x != 0) fprintf(stderr, "(e%d, %f)\t", i + 1, pmodel->m_vTheta[i]);
}
glp_delete_prob(lp);
return 1;
}
bool isFeasible()
{
int nCons;
int coef1[MAXNEDGES], coef2[MAXNEDGES];
int ind[MAXNEDGES+1];
double val[MAXNEDGES+1];
glp_prob * lp = glp_create_prob();
glp_set_obj_dir(lp, GLP_MAX);
glp_add_cols(lp, nEdges+1);
for (int i=0; i<nEdges+1; i++)
glp_set_col_bnds(lp, i+1, GLP_LO, 0, 0);
glp_set_obj_coef(lp, nEdges+1, 1);
nCons = 0;
for (int i=0; i<nEdges; i++) {
nCons++;
glp_add_rows(lp,1);
glp_set_row_bnds(lp, nCons, GLP_UP, 0, 0);
ind[1] = nEdges+1; val[1] = 1; //gamma <= l_e
ind[2] = i+1; val[2] = -1;
glp_set_mat_row(lp, nCons, 2, ind, val);
}
for (int m=0; m<k; m++) {
int first = -1;
for (int i=0; i<paths[m].n; i++) if (paths[m].isShort[i]) { first = i; break; }
assert(first>=0);
setCoef(coef1, m, first);
for (int i=first+1; i<paths[m].n; i++) if (paths[m].isShort[i]) {
setCoef(coef2, m, i);
nCons++;
glp_add_rows(lp,1);
glp_set_row_bnds(lp, nCons, GLP_FX, 0, 0);
int nEle = 0;
for (int j=0; j<nEdges; j++) if (coef1[j] ^ coef2[j]) {
nEle++;
ind[nEle] = j+1;
val[nEle] = (coef1[j])? 1:-1;
}
glp_set_mat_row(lp, nCons, nEle, ind, val);
}
for (int i=0; i<paths[m].n; i++) if (!paths[m].isShort[i]){
setCoef(coef2, m, i);
nCons++;
glp_add_rows(lp,1);
glp_set_row_bnds(lp, nCons, GLP_UP, 0, 0);
int nEle = 0;
for (int j=0; j<nEdges; j++) if (coef1[j] ^ coef2[j]) {
nEle++;
ind[nEle] = j+1;
val[nEle] = (coef1[j])? 1:-1;
}
glp_set_mat_row(lp, nCons, nEle, ind, val);
}
}
nCons++;
glp_add_rows(lp,1);
glp_set_row_bnds(lp, nCons, GLP_UP, 0, 1);
for (int j=0; j<nEdges; j++) {
ind[j+1] = j+1;
val[j+1] = 1;
}
glp_set_mat_row(lp, nCons, nEdges, ind, val);
glp_term_out(GLP_OFF);
glp_simplex(lp, NULL);
double ret = glp_get_obj_val(lp);
glp_delete_prob(lp);
return (ret>0);
}
int main(int argc, char *argv[])
{
/* structures de données propres à GLPK */
glp_prob *prob; // déclaration d'un pointeur sur le problème
int ia[1 + NBCREUX];
int ja[1 + NBCREUX];
double ar[1 + NBCREUX]; // déclaration des 3 tableaux servant à définir la partie creuse de la matrice des contraintes
int p[N+1];
p[1] = 34;
p[2] = 6; p[3] = 8; p[4] = 17; p[5] = 16; p[6] = 5; p[7] = 13; p[8] = 21; p[9] = 25; p[10] = 31; p[11] = 14; p[12] = 13; p[13] = 33; p[14] = 9; p[15] = 25; p[16] = 25;
/* variables récupérant les résultats de la résolution du problème (fonction objectif et valeur des variables) */
int i,j,pos;
double z;
double x[NBVAR];
/* Les déclarations suivantes sont optionnelles, leur but est de donner des noms aux variables et aux contraintes.
Cela permet de lire plus facilement le modèle saisi si on en demande un affichage à GLPK, ce qui est souvent utile pour détecter une erreur! */
char nomcontr[NBCONTR][8]; /* ici, les contraintes seront nommées "caisse1", "caisse2",... */
char numero[NBCONTR][3]; /* pour un nombre à deux chiffres */
char nomvar[NBVAR][3]; /* "xA", "xB", ... */
/* Création d'un problème (initialement vide) */
prob = glp_create_prob(); /* allocation mémoire pour le problème */
glp_set_prob_name(prob, "wagons"); /* affectation d'un nom (on pourrait mettre NULL) */
glp_set_obj_dir(prob, GLP_MIN); /* Il s'agit d'un problème de minimisation, on utiliserait la constante GLP_MAX dans le cas contraire */
/* Déclaration du nombre de contraintes (nombre de lignes de la matrice des contraintes) : NBCONTR */
glp_add_rows(prob, NBCONTR);
/* On commence par préciser les bornes sur les constrainte, les indices des contraintes commencent à 1 (!) dans GLPK */
for(i = 1;i <= N;i++)
{
/* partie optionnelle : donner un nom aux contraintes */
strcpy(nomcontr[i-1], "caisse");
sprintf(numero[i-1], "%d", i);
strcat(nomcontr[i-1], numero[i-1]); /* Les contraintes sont nommés "salle1", "salle2"... */
glp_set_row_name(prob, i, nomcontr[i-1]); /* Affectation du nom à la contrainte i */
/* partie indispensable : les bornes sur les contraintes */
glp_set_row_bnds(prob, i, GLP_FX, 1.0, 1.0);
}
for(i = N+1;i <= NBCONTR;i++)
{
/* partie optionnelle : donner un nom aux contraintes */
strcpy(nomcontr[i-1], "chaMax");
sprintf(numero[i-1], "%d", i);
strcat(nomcontr[i-1], numero[i-1]); /* Les contraintes sont nommés "chargemax", "chargemax2"... */
glp_set_row_name(prob, i, nomcontr[i-1]); /* Affectation du nom à la contrainte i */
// il doit manquer un bout ici
glp_set_row_bnds(prob, i, GLP_UP, 0.0, 0.0);
//<=0
// on met cmax a gauche car c'est une variable
// il aura le coeff -1 dans la mat creuse
}
/* Déclaration du nombre de variables : NBVAR */
glp_add_cols(prob, NBVAR);
/* On précise le type des variables, les indices commencent à 1 également pour les variables! */
for(i = 1;i <= NBVAR;i++)
{
if(i==NBVAR){
sprintf(nomvar[i-1],"Cm");
glp_set_col_name(prob, i , nomvar[i-1]);
glp_set_col_bnds(prob, i, GLP_LO, 0.0, 0.0);
}else{
/* partie optionnelle : donner un nom aux variables */
sprintf(nomvar[i-1],"x%d",i-1);
glp_set_col_name(prob, i , nomvar[i-1]); /* Les variables sont nommées "xA", "xB"... afin de respecter les noms de variables de l'exercice 2.2 */
/* partie obligatoire : bornes éventuelles sur les variables, et type */
glp_set_col_bnds(prob, i, GLP_DB, 0.0, 1.0); /* bornes sur les variables, comme sur les contraintes */
glp_set_col_kind(prob, i, GLP_BV); /* les variables sont par défaut continues, nous précisons ici qu'elles sont binaires avec la constante GLP_BV, on utiliserait GLP_IV pour des variables entières */
}
}
/* définition des coefficients des variables dans la fonction objectif */
for(i = 1;i <= N*M;i++) glp_set_obj_coef(prob,i,0.0); // Tous les coûts sont ici à 0! Mais on doit specifier quand meme
glp_set_obj_coef(prob,N*M+1,1.0); // 1 fois cmax
/* Définition des coefficients non-nuls dans la matrice des contraintes, autrement dit les coefficients de la matrice creuse */
/* Les indices commencent également à 1 ! */
// pour i de 1 a n
//pour i de 1 a m
/* xij intervient dans la ligne i avec un coeff 1 et dans la ligne
n+j avec un coeff pi
ia -> i et n+j
ar -> 1 et pi
ja -> xij -> (i-1)*m+j
*/
pos = 1;
for(i=1; i<=N; i++){
for(j=1; j<=M; j++){
ia[pos] = i;
ja[pos] = (i-1)*M+j; ar[pos] = 1;
pos++;
ia[pos] = N+j;
ja[pos] = (i-1)*M+j; ar[pos] = p[i];
pos++;
}
}
//Cmax a -1 !!!
for(i=N+1; i<=N+M;i++){
ia[pos] = i;
ja[pos] = N*M+1; ar[pos] = -1;
pos++;
}
/* chargement de la matrice dans le problème */
glp_load_matrix(prob,NBCREUX,ia,ja,ar);
/* Optionnel : écriture de la modélisation dans un fichier (TRES utile pour debugger!) */
glp_write_lp(prob,NULL,"wagons.lp");
/* Résolution, puis lecture des résultats */
glp_simplex(prob,NULL); glp_intopt(prob,NULL); /* Résolution */
z = glp_mip_obj_val(prob); /* Récupération de la valeur optimale. Dans le cas d'un problème en variables continues, l'appel est différent : z = glp_get_obj_val(prob); */
for(i = 0;i < NBVAR; i++) x[i] = glp_mip_col_val(prob,i+1); /* Récupération de la valeur des variables, Appel différent dans le cas d'un problème en variables continues : for(i = 0;i < p.nbvar;i++) x[i] = glp_get_col_prim(prob,i+1); */
printf("z = %lf\n",z);
for(i = 0;i < NBVAR;i++) printf("x%d = %d, ",i,(int)(x[i] + 0.5)); /* un cast est ajouté, x[i] pourrait être égal à 0.99999... */
puts("");
/* libération mémoire */
glp_delete_prob(prob);
/* J'adore qu'un plan se déroule sans accroc! */
return 0;
}
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
double *a, double *b, char *ctype, int *freeLB, double *lb,
int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
int save_pb, char *save_filename, char *filetype,
double *xmin, double *fmin, double *status,
double *lambda, double *redcosts, double *time, double *mem)
{
int typx = 0;
int method;
clock_t t_start = clock();
// Obsolete
//lib_set_fault_hook (NULL, glpk_fault_hook);
//Redirect standard output
if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
else glp_term_hook (NULL, NULL);
//-- Create an empty LP/MILP object
glp_prob *lp = glp_create_prob ();
//-- Set the sense of optimization
if (sense == 1)
glp_set_obj_dir (lp, GLP_MIN);
else
glp_set_obj_dir (lp, GLP_MAX);
//-- Define the number of unknowns and their domains.
glp_add_cols (lp, n);
for (int i = 0; i < n; i++)
{
//-- Define type of the structural variables
if (! freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
else
{
if (! freeLB[i] && freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
else
{
if (freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
}
}
// -- Set the objective coefficient of the corresponding
// -- structural variable. No constant term is assumed.
glp_set_obj_coef(lp,i+1,c[i]);
if (isMIP)
glp_set_col_kind (lp, i+1, vartype[i]);
}
glp_add_rows (lp, m);
for (int i = 0; i < m; i++)
{
/* If the i-th row has no lower bound (types F,U), the
corrispondent parameter will be ignored.
If the i-th row has no upper bound (types F,L), the corrispondent
parameter will be ignored.
If the i-th row is of S type, the i-th LB is used, but
the i-th UB is ignored.
*/
switch (ctype[i])
{
case 'F': typx = GLP_FR; break;
// upper bound
case 'U': typx = GLP_UP; break;
// lower bound
case 'L': typx = GLP_LO; break;
// fixed constraint
case 'S': typx = GLP_FX; break;
// double-bounded variable
case 'D': typx = GLP_DB; break;
}
glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
}
// Load constraint matrix A
glp_load_matrix (lp, nz, rn, cn, a);
// Save problem
if (save_pb) {
if (!strcmp(filetype,"cplex")){
if (lpx_write_cpxlp (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"fixedmps")){
if (lpx_write_mps (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"freemps")){
if (lpx_write_freemps (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{// plain text
if (lpx_print_prob (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}
}
}
}
//-- scale the problem data (if required)
if (glpIntParam[1] && (! glpIntParam[16] || lpsolver != 1))
lpx_scale_prob (lp);
//-- build advanced initial basis (if required)
if (lpsolver == 1 && ! glpIntParam[16])
lpx_adv_basis (lp);
glp_smcp sParam;
glp_init_smcp(&sParam);
//-- set control parameters
if (lpsolver==1){
//remap of control parameters for simplex method
sParam.msg_lev=glpIntParam[0]; // message level
// simplex method: primal/dual
if (glpIntParam[2]==0) sParam.meth=GLP_PRIMAL;
else sParam.meth=GLP_DUALP;
// pricing technique
if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
else sParam.pricing=GLP_PT_PSE;
//sParam.r_test not available
sParam.tol_bnd=glpRealParam[1]; // primal feasible tollerance
sParam.tol_dj=glpRealParam[2]; // dual feasible tollerance
sParam.tol_piv=glpRealParam[3]; // pivot tollerance
sParam.obj_ll=glpRealParam[4]; // lower limit
sParam.obj_ul=glpRealParam[5]; // upper limit
// iteration limit
if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
else sParam.it_lim=glpIntParam[5];
// time limit
if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
else sParam.tm_lim=(int) glpRealParam[6];
sParam.out_frq=glpIntParam[7]; // output frequency
sParam.out_dly=(int) glpRealParam[7]; // output delay
// presolver
if (glpIntParam[16]) sParam.presolve=GLP_ON;
else sParam.presolve=GLP_OFF;
}else{
for(int i = 0; i < NIntP; i++)
lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
for (int i = 0; i < NRealP; i++)
lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
}
// Choose simplex method ('S') or interior point method ('T') to solve the problem
if (lpsolver == 1)
method = 'S';
else
method = 'T';
int errnum;
switch (method){
case 'S': {
if (isMIP){
method = 'I';
errnum = lpx_intopt (lp);
}
else{
errnum = glp_simplex(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
}
}
break;
case 'T': errnum = lpx_interior(lp); break;
default: xassert (method != method);
}
/* errnum assumes the following results:
errnum = 0 <=> No errors
errnum = 1 <=> Iteration limit exceeded.
errnum = 2 <=> Numerical problems with basis matrix.
*/
if (errnum == LPX_E_OK || errnum==100){
// Get status and object value
if (isMIP)
{
*status = glp_mip_status (lp);
*fmin = glp_mip_obj_val (lp);
}
else
{
if (lpsolver == 1)
{
*status = glp_get_status (lp);
*fmin = glp_get_obj_val (lp);
}
else
{
*status = glp_ipt_status (lp);
*fmin = glp_ipt_obj_val (lp);
}
}
// Get optimal solution (if exists)
if (isMIP)
{
for (int i = 0; i < n; i++)
xmin[i] = glp_mip_col_val (lp, i+1);
}
else
{
/* Primal values */
for (int i = 0; i < n; i++)
{
if (lpsolver == 1)
xmin[i] = glp_get_col_prim (lp, i+1);
else
xmin[i] = glp_ipt_col_prim (lp, i+1);
}
/* Dual values */
for (int i = 0; i < m; i++)
{
if (lpsolver == 1) lambda[i] = glp_get_row_dual (lp, i+1);
else lambda[i] = glp_ipt_row_dual (lp, i+1);
}
/* Reduced costs */
for (int i = 0; i < glp_get_num_cols (lp); i++)
{
if (lpsolver == 1) redcosts[i] = glp_get_col_dual (lp, i+1);
else redcosts[i] = glp_ipt_col_dual (lp, i+1);
}
}
*time = (clock () - t_start) / CLOCKS_PER_SEC;
glp_ulong tpeak;
lib_mem_usage(NULL, NULL, NULL, &tpeak);
*mem=(double)(4294967296.0 * tpeak.hi + tpeak.lo) / (1024);
glp_delete_prob (lp);
return 0;
}
glp_delete_prob (lp);
*status = errnum;
return errnum;
}
int main(int argc, char *argv[]) {
/* Structures de données propres à GLPK */
glp_prob *prob; // Déclaration d'un pointeur sur le problème
int ia[1 + NBCREUX];
int ja[1 + NBCREUX];
double ar[1 + NBCREUX]; // Déclaration des 3 tableaux servant à définir la partie creuse de la matrice des contraintes
/* Variables récupérant les résultats de la résolution du problème (fonction objectif et valeur des variables) */
int i, j;
double z;
double x[NBVAR];
// Autres variables
int * p = (int*)malloc(n * sizeof(int));
p[1] = 34;
p[2] = 6;
p[3] = 8;
p[4] = 17;
p[5] = 16;
p[6] = 5;
p[7] = 13;
p[8] = 21;
p[9] = 25;
p[10] = 31;
p[11] = 14;
p[12] = 13;
p[13] = 33;
p[14] = 9;
p[15] = 25;
p[16] = 25;
/* Transfert de ces données dans les structures utilisées par la bibliothèque GLPK */
prob = glp_create_prob(); /* allocation mémoire pour le problème */
glp_set_prob_name(prob, "wagons"); /* affectation d'un nom */
glp_set_obj_dir(prob, GLP_MIN); /* Il s'agit d'un problème de minimisation */
/* Déclaration du nombre de contraintes (nombre de lignes de la matrice des contraintes) */
glp_add_rows(prob, NBCONTR);
/* On commence par préciser les bornes sur les contraintes, les indices commencent à 1 (!) dans GLPK */
/* Premier ensemble de contraintes ( c = 1 ) */
for(i = 1; i <= n; i++) {
glp_set_row_bnds(prob, i, GLP_FX, 1.0, 1.0);
}
/* Second ensembles de contraintes (c <= 0 ) */
for(i = n + 1; i <= NBCONTR; i++) {
glp_set_row_bnds(prob, i, GLP_UP, 0.0, 0.0);
}
/* Déclaration du nombre de variables */
glp_add_cols(prob, NBVAR);
/* On précise le type des variables, les indices commencent à 1 également pour les variables! */
for(i = 1; i <= NBVAR - 1; i++) {
glp_set_col_bnds(prob, i, GLP_DB, 0.0, 1.0);
glp_set_col_kind(prob, i, GLP_BV); /* les variables sont binaires */
}
glp_set_col_bnds(prob, NBVAR, GLP_LO, 0.0, 0.0); /* La dernière variables est continue (par défaut) non négative */
/* Définition des coefficients des variables dans la fonction objectif */
for(i = 1;i <= n*m;i++) {
glp_set_obj_coef(prob,i,0.0); // Tous les coûts sont à 0 (sauf le dernier)
}
/* Dernier coût (qui vaut 1) */
glp_set_obj_coef(prob,n*m + 1,1.0);
/* Définition des coefficients non-nuls dans la matrice des contraintes, autrement dit les coefficients de la matrice creuse */
int pos = 1;
for(i = 1; i <= n; i++) {
for(j = 1; j <= m; j++) {
// Première moitié de la matrice
ja[pos] = (i - 1)*m + j;
ia[pos] = i;
ar[pos] = 1;
pos++;
// Deuxième moitié de la matrice
ja[pos] = (i - 1)*m + j;
ia[pos] = n + j;
ar[pos] = p[i];
pos++;
}
}
// ajout des -1 dans la dernière colonne
for(i = n + 1; i <= n + m; i++) {
ja[pos] = n*m + 1;
ia[pos] = i;
ar[pos] = -1;
pos++;
}
/* Chargement de la matrice dans le problème */
glp_load_matrix(prob,NBCREUX,ia,ja,ar);
/* Ecriture de la modélisation dans un fichier */
glp_write_lp(prob,NULL,"wagons.lp");
/* Résolution, puis lecture des résultats */
glp_simplex(prob,NULL); glp_intopt(prob,NULL); /* Résolution */
z = glp_mip_obj_val(prob); /* Récupération de la valeur optimale. Dans le cas d'un problème en variables continues, l'appel est différent : z = glp_get_obj_val(prob); */
for(i = 0;i < NBVAR; i++) x[i] = glp_mip_col_val(prob,i+1); /* Récupération de la valeur des variables, Appel différent dans le cas d'un problème en variables continues : for(i = 0;i < p.nbvar;i++) x[i] = glp_get_col_prim(prob,i+1); */
printf("z = %lf\n",z);
for(i = 0;i < NBVAR;i++) printf("x%c = %d, ",'B'+i,(int)(x[i] + 0.5)); /* un cast est ajouté, x[i] pourrait être égal à 0.99999... */
puts("");
/* Libération de la mémoire */
glp_delete_prob(prob);
free(p);
return 0;
}
int glp_read_prob(glp_prob *P, int flags, const char *fname)
{ DMX _csa, *csa = &_csa;
int mip, m, n, nnz, ne, i, j, k, type, kind, ret, *ln = NULL,
*ia = NULL, *ja = NULL;
double lb, ub, temp, *ar = NULL;
char *rf = NULL, *cf = NULL;
if (P == NULL || P->magic != GLP_PROB_MAGIC)
xerror("glp_read_prob: P = %p; invalid problem object\n",
P);
if (flags != 0)
xerror("glp_read_prob: flags = %d; invalid parameter\n",
flags);
if (fname == NULL)
xerror("glp_read_prob: fname = %d; invalid parameter\n",
fname);
glp_erase_prob(P);
if (setjmp(csa->jump))
{ ret = 1;
goto done;
}
csa->fname = fname;
csa->fp = NULL;
csa->count = 0;
csa->c = '\n';
csa->field[0] = '\0';
csa->empty = csa->nonint = 0;
xprintf("Reading problem data from '%s'...\n", fname);
csa->fp = glp_open(fname, "r");
if (csa->fp == NULL)
{ xprintf("Unable to open '%s' - %s\n", fname, get_err_msg());
longjmp(csa->jump, 1);
}
/* read problem line */
read_designator(csa);
if (strcmp(csa->field, "p") != 0)
error(csa, "problem line missing or invalid");
read_field(csa);
if (strcmp(csa->field, "lp") == 0)
mip = 0;
else if (strcmp(csa->field, "mip") == 0)
mip = 1;
else
error(csa, "wrong problem designator; 'lp' or 'mip' expected");
read_field(csa);
if (strcmp(csa->field, "min") == 0)
glp_set_obj_dir(P, GLP_MIN);
else if (strcmp(csa->field, "max") == 0)
glp_set_obj_dir(P, GLP_MAX);
else
error(csa, "objective sense missing or invalid");
read_field(csa);
if (!(str2int(csa->field, &m) == 0 && m >= 0))
error(csa, "number of rows missing or invalid");
read_field(csa);
if (!(str2int(csa->field, &n) == 0 && n >= 0))
error(csa, "number of columns missing or invalid");
read_field(csa);
if (!(str2int(csa->field, &nnz) == 0 && nnz >= 0))
error(csa, "number of constraint coefficients missing or inval"
"id");
if (m > 0)
{ glp_add_rows(P, m);
for (i = 1; i <= m; i++)
glp_set_row_bnds(P, i, GLP_FX, 0.0, 0.0);
}
if (n > 0)
{ glp_add_cols(P, n);
for (j = 1; j <= n; j++)
{ if (!mip)
glp_set_col_bnds(P, j, GLP_LO, 0.0, 0.0);
else
glp_set_col_kind(P, j, GLP_BV);
}
}
end_of_line(csa);
/* allocate working arrays */
rf = xcalloc(1+m, sizeof(char));
memset(rf, 0, 1+m);
cf = xcalloc(1+n, sizeof(char));
memset(cf, 0, 1+n);
ln = xcalloc(1+nnz, sizeof(int));
ia = xcalloc(1+nnz, sizeof(int));
ja = xcalloc(1+nnz, sizeof(int));
ar = xcalloc(1+nnz, sizeof(double));
/* read descriptor lines */
ne = 0;
for (;;)
{ read_designator(csa);
if (strcmp(csa->field, "i") == 0)
{ /* row descriptor */
read_field(csa);
if (str2int(csa->field, &i) != 0)
error(csa, "row number missing or invalid");
if (!(1 <= i && i <= m))
error(csa, "row number out of range");
read_field(csa);
if (strcmp(csa->field, "f") == 0)
type = GLP_FR;
else if (strcmp(csa->field, "l") == 0)
type = GLP_LO;
else if (strcmp(csa->field, "u") == 0)
type = GLP_UP;
else if (strcmp(csa->field, "d") == 0)
type = GLP_DB;
else if (strcmp(csa->field, "s") == 0)
type = GLP_FX;
else
error(csa, "row type missing or invalid");
if (type == GLP_LO || type == GLP_DB || type == GLP_FX)
{ read_field(csa);
if (str2num(csa->field, &lb) != 0)
error(csa, "row lower bound/fixed value missing or in"
"valid");
}
else
lb = 0.0;
if (type == GLP_UP || type == GLP_DB)
{ read_field(csa);
if (str2num(csa->field, &ub) != 0)
error(csa, "row upper bound missing or invalid");
}
else
ub = 0.0;
if (rf[i] & 0x01)
error(csa, "duplicate row descriptor");
glp_set_row_bnds(P, i, type, lb, ub), rf[i] |= 0x01;
}
else if (strcmp(csa->field, "j") == 0)
{ /* column descriptor */
read_field(csa);
if (str2int(csa->field, &j) != 0)
error(csa, "column number missing or invalid");
if (!(1 <= j && j <= n))
error(csa, "column number out of range");
if (!mip)
kind = GLP_CV;
else
{ read_field(csa);
if (strcmp(csa->field, "c") == 0)
kind = GLP_CV;
else if (strcmp(csa->field, "i") == 0)
kind = GLP_IV;
else if (strcmp(csa->field, "b") == 0)
{ kind = GLP_IV;
type = GLP_DB, lb = 0.0, ub = 1.0;
goto skip;
}
else
error(csa, "column kind missing or invalid");
}
read_field(csa);
if (strcmp(csa->field, "f") == 0)
type = GLP_FR;
else if (strcmp(csa->field, "l") == 0)
type = GLP_LO;
else if (strcmp(csa->field, "u") == 0)
type = GLP_UP;
else if (strcmp(csa->field, "d") == 0)
type = GLP_DB;
else if (strcmp(csa->field, "s") == 0)
type = GLP_FX;
else
error(csa, "column type missing or invalid");
if (type == GLP_LO || type == GLP_DB || type == GLP_FX)
{ read_field(csa);
if (str2num(csa->field, &lb) != 0)
error(csa, "column lower bound/fixed value missing or"
" invalid");
}
else
lb = 0.0;
if (type == GLP_UP || type == GLP_DB)
{ read_field(csa);
if (str2num(csa->field, &ub) != 0)
error(csa, "column upper bound missing or invalid");
}
else
ub = 0.0;
skip: if (cf[j] & 0x01)
error(csa, "duplicate column descriptor");
glp_set_col_kind(P, j, kind);
glp_set_col_bnds(P, j, type, lb, ub), cf[j] |= 0x01;
}
else if (strcmp(csa->field, "a") == 0)
{ /* coefficient descriptor */
read_field(csa);
if (str2int(csa->field, &i) != 0)
error(csa, "row number missing or invalid");
if (!(0 <= i && i <= m))
error(csa, "row number out of range");
read_field(csa);
if (str2int(csa->field, &j) != 0)
error(csa, "column number missing or invalid");
if (!((i == 0 ? 0 : 1) <= j && j <= n))
error(csa, "column number out of range");
read_field(csa);
if (i == 0)
{ if (str2num(csa->field, &temp) != 0)
error(csa, "objective %s missing or invalid",
j == 0 ? "constant term" : "coefficient");
if (cf[j] & 0x10)
error(csa, "duplicate objective %s",
j == 0 ? "constant term" : "coefficient");
glp_set_obj_coef(P, j, temp), cf[j] |= 0x10;
}
else
{ if (str2num(csa->field, &temp) != 0)
error(csa, "constraint coefficient missing or invalid"
);
if (ne == nnz)
error(csa, "too many constraint coefficient descripto"
"rs");
ln[++ne] = csa->count;
ia[ne] = i, ja[ne] = j, ar[ne] = temp;
}
}
else if (strcmp(csa->field, "n") == 0)
{ /* symbolic name descriptor */
read_field(csa);
if (strcmp(csa->field, "p") == 0)
{ /* problem name */
read_field(csa);
if (P->name != NULL)
error(csa, "duplicate problem name");
glp_set_prob_name(P, csa->field);
}
else if (strcmp(csa->field, "z") == 0)
{ /* objective name */
read_field(csa);
if (P->obj != NULL)
error(csa, "duplicate objective name");
glp_set_obj_name(P, csa->field);
}
else if (strcmp(csa->field, "i") == 0)
{ /* row name */
read_field(csa);
if (str2int(csa->field, &i) != 0)
error(csa, "row number missing or invalid");
if (!(1 <= i && i <= m))
error(csa, "row number out of range");
read_field(csa);
if (P->row[i]->name != NULL)
error(csa, "duplicate row name");
glp_set_row_name(P, i, csa->field);
}
else if (strcmp(csa->field, "j") == 0)
{ /* column name */
read_field(csa);
if (str2int(csa->field, &j) != 0)
error(csa, "column number missing or invalid");
if (!(1 <= j && j <= n))
error(csa, "column number out of range");
read_field(csa);
if (P->col[j]->name != NULL)
error(csa, "duplicate column name");
glp_set_col_name(P, j, csa->field);
}
else
error(csa, "object designator missing or invalid");
}
else if (strcmp(csa->field, "e") == 0)
break;
else
error(csa, "line designator missing or invalid");
end_of_line(csa);
}
if (ne < nnz)
error(csa, "too few constraint coefficient descriptors");
xassert(ne == nnz);
k = glp_check_dup(m, n, ne, ia, ja);
xassert(0 <= k && k <= nnz);
if (k > 0)
{ csa->count = ln[k];
error(csa, "duplicate constraint coefficient");
}
glp_load_matrix(P, ne, ia, ja, ar);
/* print some statistics */
if (P->name != NULL)
xprintf("Problem: %s\n", P->name);
if (P->obj != NULL)
xprintf("Objective: %s\n", P->obj);
xprintf("%d row%s, %d column%s, %d non-zero%s\n",
m, m == 1 ? "" : "s", n, n == 1 ? "" : "s", nnz, nnz == 1 ?
"" : "s");
if (glp_get_num_int(P) > 0)
{ int ni = glp_get_num_int(P);
int nb = glp_get_num_bin(P);
if (ni == 1)
{ if (nb == 0)
xprintf("One variable is integer\n");
else
xprintf("One variable is binary\n");
}
else
{ xprintf("%d integer variables, ", ni);
if (nb == 0)
xprintf("none");
else if (nb == 1)
xprintf("one");
else if (nb == ni)
xprintf("all");
else
xprintf("%d", nb);
xprintf(" of which %s binary\n", nb == 1 ? "is" : "are");
}
}
xprintf("%d lines were read\n", csa->count);
/* problem data has been successfully read */
glp_sort_matrix(P);
ret = 0;
done: if (csa->fp != NULL) glp_close(csa->fp);
if (rf != NULL) xfree(rf);
if (cf != NULL) xfree(cf);
if (ln != NULL) xfree(ln);
if (ia != NULL) xfree(ia);
if (ja != NULL) xfree(ja);
if (ar != NULL) xfree(ar);
if (ret) glp_erase_prob(P);
return ret;
}
void glp_maxflow_lp(glp_prob *lp, glp_graph *G, int names, int s,
int t, int a_cap)
{ glp_vertex *v;
glp_arc *a;
int i, j, type, ind[1+2];
double cap, val[1+2];
if (!(names == GLP_ON || names == GLP_OFF))
xerror("glp_maxflow_lp: names = %d; invalid parameter\n",
names);
if (!(1 <= s && s <= G->nv))
xerror("glp_maxflow_lp: s = %d; source node number out of rang"
"e\n", s);
if (!(1 <= t && t <= G->nv))
xerror("glp_maxflow_lp: t = %d: sink node number out of range "
"\n", t);
if (s == t)
xerror("glp_maxflow_lp: s = t = %d; source and sink nodes must"
" be distinct\n", s);
if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double))
xerror("glp_maxflow_lp: a_cap = %d; invalid offset\n", a_cap);
glp_erase_prob(lp);
if (names) glp_set_prob_name(lp, G->name);
glp_set_obj_dir(lp, GLP_MAX);
glp_add_rows(lp, G->nv);
for (i = 1; i <= G->nv; i++)
{ v = G->v[i];
if (names) glp_set_row_name(lp, i, v->name);
if (i == s)
type = GLP_LO;
else if (i == t)
type = GLP_UP;
else
type = GLP_FX;
glp_set_row_bnds(lp, i, type, 0.0, 0.0);
}
if (G->na > 0) glp_add_cols(lp, G->na);
for (i = 1, j = 0; i <= G->nv; i++)
{ v = G->v[i];
for (a = v->out; a != NULL; a = a->t_next)
{ j++;
if (names)
{ char name[50+1];
sprintf(name, "x[%d,%d]", a->tail->i, a->head->i);
xassert(strlen(name) < sizeof(name));
glp_set_col_name(lp, j, name);
}
if (a->tail->i != a->head->i)
{ ind[1] = a->tail->i, val[1] = +1.0;
ind[2] = a->head->i, val[2] = -1.0;
glp_set_mat_col(lp, j, 2, ind, val);
}
if (a_cap >= 0)
memcpy(&cap, (char *)a->data + a_cap, sizeof(double));
else
cap = 1.0;
if (cap == DBL_MAX)
type = GLP_LO;
else if (cap != 0.0)
type = GLP_DB;
else
type = GLP_FX;
glp_set_col_bnds(lp, j, type, 0.0, cap);
if (a->tail->i == s)
glp_set_obj_coef(lp, j, +1.0);
else if (a->head->i == s)
glp_set_obj_coef(lp, j, -1.0);
}
}
xassert(j == G->na);
return;
}
void inline instrumentert::instrument_minimum_interference_inserter(
const std::set<event_grapht::critical_cyclet>& set_of_cycles)
{
/* Idea:
We solve this by a linear programming approach,
using for instance glpk lib.
Input: the edges to instrument E, the cycles C_j
Pb: min sum_{e_i in E} d(e_i).x_i
s.t. for all j, sum_{e_i in C_j} >= 1,
where e_i is a pair to potentially instrument,
x_i is a Boolean stating whether we instrument
e_i, and d() is the cost of an instrumentation.
Output: the x_i, saying which pairs to instrument
For this instrumentation, we propose:
d(poW*)=1
d(poRW)=d(rfe)=2
d(poRR)=3
This function can be refined with the actual times
we get in experimenting the different pairs in a
single IRIW.
*/
#ifdef HAVE_GLPK
/* first, identify all the unsafe pairs */
std::set<event_grapht::critical_cyclet::delayt> edges;
for(std::set<event_grapht::critical_cyclet>::iterator
C_j=set_of_cycles.begin();
C_j!=set_of_cycles.end();
++C_j)
for(std::set<event_grapht::critical_cyclet::delayt>::const_iterator e_i=
C_j->unsafe_pairs.begin();
e_i!=C_j->unsafe_pairs.end();
++e_i)
edges.insert(*e_i);
glp_prob* lp;
glp_iocp parm;
glp_init_iocp(&parm);
parm.msg_lev=GLP_MSG_OFF;
parm.presolve=GLP_ON;
lp=glp_create_prob();
glp_set_prob_name(lp, "instrumentation optimisation");
glp_set_obj_dir(lp, GLP_MIN);
message.debug() << "edges: "<<edges.size()<<" cycles:"<<set_of_cycles.size()
<< messaget::eom;
/* sets the variables and coefficients */
glp_add_cols(lp, edges.size());
unsigned i=0;
for(std::set<event_grapht::critical_cyclet::delayt>::iterator
e_i=edges.begin();
e_i!=edges.end();
++e_i)
{
++i;
std::string name="e_"+i2string(i);
glp_set_col_name(lp, i, name.c_str());
glp_set_col_bnds(lp, i, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, i, cost(*e_i));
glp_set_col_kind(lp, i, GLP_BV);
}
/* sets the constraints (soundness): one per cycle */
glp_add_rows(lp, set_of_cycles.size());
i=0;
for(std::set<event_grapht::critical_cyclet>::iterator
C_j=set_of_cycles.begin();
C_j!=set_of_cycles.end();
++C_j)
{
++i;
std::string name="C_"+i2string(i);
glp_set_row_name(lp, i, name.c_str());
glp_set_row_bnds(lp, i, GLP_LO, 1.0, 0.0); /* >= 1*/
}
const unsigned mat_size=set_of_cycles.size()*edges.size();
message.debug() << "size of the system: " << mat_size
<< messaget::eom;
int* imat=(int*)malloc(sizeof(int)*(mat_size+1));
int* jmat=(int*)malloc(sizeof(int)*(mat_size+1));
double* vmat=(double*)malloc(sizeof(double)*(mat_size+1));
/* fills the constraints coeff */
/* tables read from 1 in glpk -- first row/column ignored */
unsigned col=1;
unsigned row=1;
i=1;
for(std::set<event_grapht::critical_cyclet::delayt>::iterator
e_i=edges.begin();
e_i!=edges.end();
++e_i)
{
row=1;
for(std::set<event_grapht::critical_cyclet>::iterator
C_j=set_of_cycles.begin();
C_j!=set_of_cycles.end();
++C_j)
{
imat[i]=row;
jmat[i]=col;
if(C_j->unsafe_pairs.find(*e_i)!=C_j->unsafe_pairs.end())
vmat[i]=1.0;
else
vmat[i]=0.0;
++i;
++row;
}
++col;
}
#ifdef DEBUG
for(i=1; i<=mat_size; ++i)
message.statistics() <<i<<"["<<imat[i]<<","<<jmat[i]<<"]="<<vmat[i]
<< messaget::eom;
#endif
/* solves MIP by branch-and-cut */
glp_load_matrix(lp, mat_size, imat, jmat, vmat);
glp_intopt(lp, &parm);
/* loads results (x_i) */
message.statistics() << "minimal cost: " << glp_mip_obj_val(lp)
<< messaget::eom;
i=0;
for(std::set<event_grapht::critical_cyclet::delayt>::iterator
e_i=edges.begin();
e_i!=edges.end();
++e_i)
{
++i;
if(glp_mip_col_val(lp, i)>=1)
{
const abstract_eventt& first_ev=egraph[e_i->first];
var_to_instr.insert(first_ev.variable);
id2loc.insert(
std::pair<irep_idt,source_locationt>(first_ev.variable,first_ev.source_location));
if(!e_i->is_po)
{
const abstract_eventt& second_ev=egraph[e_i->second];
var_to_instr.insert(second_ev.variable);
id2loc.insert(
std::pair<irep_idt,source_locationt>(second_ev.variable,second_ev.source_location));
}
}
}
glp_delete_prob(lp);
free(imat);
free(jmat);
free(vmat);
#else
throw "Sorry, minimum interference option requires glpk; "
"please recompile goto-instrument with glpk.";
#endif
}
int main()
{
// Variáveis auxiliares
int i, j, constraintNumber, *constraintIndices;
double *constraintCoefficients;
// Aloca os vetores utilizados para criar as restrições do problema
// ***********************************************************************************************
// ATENÇÃO ===> É importante dizer que estes vetores serão utilizados da posição 1 em diante
// Ou seja, no GLPK você aloca uma posição a mais e descarta a posição 0 dos vetores.
// ***********************************************************************************************
constraintIndices = (int*)malloc((n+1)*sizeof(int));
constraintCoefficients = (double*)malloc((n+1)*sizeof(double));
// Cria um modelo com nenhuma variável e nenhuma restrição
glp_prob *model = glp_create_prob();
// Define o sentido da otimização que, para este problema, é minimização
glp_set_obj_dir(model, GLP_MIN);
// Cria as variáveis (colunas) no modelo
// Para este problema são necessárias n*n variáveis
// Estas n*n variáveis são definidas pelo GLPK através dos indices que vão de 1 até n*n (x1, x2, ..., x(n*n))
// Portanto, neste momento é importante determinar qual variável no GLPK (índice) representará qual variável x[i,j]
// Para tanto, fazemos o mapeamento das variáveis x[i,j] utilizando a fórmula (i-1)*n + j
// Isto é, a variável x[i,j] será representada pela variável de índice (i-1)*n + j no modelo do GLPK
// Note que é imprescindível que cada índice (variável do GLPK) seja associado a no máximo uma variável x[i,j]
// Caso contrário, uma variável do GLPK pode representar duas variáveis x[i,j] diferentes que assumem valores distintos nas soluções ótimas
// Neste caso, o modelo estará incorreto
glp_add_cols(model, n*n);
// Ajuste dos tipos, limitantes e coeficientes da função objetivo das variáveis do modelo
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
{
// Define o tipo da variável como sendo binária (em outros modelos poderia ser contínua (GLP_CV) ou inteira (GLP_IV))
glp_set_col_kind(model, (i-1)*n + j, GLP_BV);
// Define o limitante inferior (0) e superior (1) da variável
// Consultem no manual as outras forma para definir apenas o limitante inferior ou superior
glp_set_col_bnds(model, (i-1)*n + j, GLP_DB, 0.0, 1.0);
// Define o coeficiente da variável na função objetivo
glp_set_obj_coef(model, (i-1)*n + j, c[i-1][j-1]);
}
// Cria no modelo 2n restrições (linhas) nulas (com os coeficientes e limitantes zerados)
// Ou seja, neste momento é criada uma matriz de zeros que correspondem aos coeficientes das restrições do modelo
// O próximo passo será modificar esta matriz de tal forma que ela represente as restrições do problema descrito
glp_add_rows(model, 2*n);
// Esta variável define qual das restrições (qual linha da matriz de coeficientes) estamos modificando
// ************************************************************************************************************
// ATENÇÃO: perceba que as restrições (linhas), assim como as variáveis (colunas), são indexadas a partir de 1.
// ************************************************************************************************************
constraintNumber = 1;
// Preenchimento das restrições limitando a soma das linhas:
// sum{j in 1..n} w[i,j]*x[i,j] <= u[i] para i in 1..n
for (i = 1; i <= n; i++)
{
// Define o limite superior (RHS) da restrição
glp_set_row_bnds(model, constraintNumber, GLP_UP, 0.0, u[i-1]);
for (j = 1; j <= n; j++)
{
// Ajusta o índice da variável que será informado à rotina do GLPK
constraintIndices[j] = (i-1)*n + j;
// Ajusta o coeficiente da variável cujo índice foi definido na linha anterior para ser informado ao GLPK
// ******************************************************************************************************
// ATENÇÃO: perceba que na matriz w os índices e colunas são indexados a partir de ZERO !
// ******************************************************************************************************
constraintCoefficients[j] = w[i-1][j-1];
}
// Passa ao GLPK a restrição que acabou de ser definida nos vetores constraintIndices e constraintCoefficients
glp_set_mat_row(model, constraintNumber, n, constraintIndices, constraintCoefficients);
// atualiza o indice da próxima restrição a ser inserida
constraintNumber++;
}
// Preenchimento das restrições limitando a soma das colunas:
// sum{i in 1..n} w[i,j]*x[i,j] >= l[i] para j in 1..n
for (j = 1; j <= n; j++)
{
// Define o limite inferior (RHS) da restrição
glp_set_row_bnds(model, constraintNumber, GLP_LO, l[j-1], 0.0);
for (i = 1; i <= n; i++)
{
// Ajusta o índice da variável que será informado a rotina do GLPK
constraintIndices[i] = (i-1)*n + j;
// Ajusta o coeficiente da variável cujo índice foi definido na linha anterior para ser informado ao GLPK
constraintCoefficients[i] = w[i-1][j-1];
}
// Passa ao GLPK a restrição que acabou de ser definida nos vetores constraintIndices e constraintCoefficients
glp_set_mat_row(model, constraintNumber, n, constraintIndices, constraintCoefficients);
// atualiza o indice da próxima restrição a ser inserida
constraintNumber++;
}
// Define os parâmetros que serão passados ao resolvedor
glp_iocp param;
glp_init_iocp(¶m);
// Ativa o presolver
param.presolve = GLP_ON;
// Resolve o modelo
int status = glp_intopt(model, ¶m);
// Verifica se houve algum erro durante a otimização
if (status)
{
printf("Ocorreu um erro durante o processo de otimizacao.\n");
}
else
{
// Verifica se o método encontrou uma solução
status = glp_mip_status(model);
if ((status == GLP_OPT) || (status == GLP_FEAS))
{
// Imprime a solução encontrada
if (status == GLP_OPT)
printf("Solucao otima encontrada!\n");
else
printf("A solucao encontrada pode nao ser otima!\n");
printf("Custo da solucao: %f\n", glp_mip_obj_val(model));
for (i = 1; i <= n; i++)
{
for (j = 1; j <= n; j++)
printf("%f ", glp_mip_col_val(model, (i-1)*n + j));
printf("\n");
}
}
else
{
printf("Nenhuma solucao foi encontrada!\n");
}
}
// Desaloca os vetores
free(constraintIndices);
free(constraintCoefficients);
return 0;
}
void ILP::sample() {
PPReader& reader = handler->get_reader();
// Read in all the data to this->data
point *p;
while ((p = reader.get())) {
data.push_back(*p);
}
delete p;
// Encode the problem into ILP
uint N = data.size();
glp_prob *lp = glp_create_prob();
glp_set_obj_dir(lp, GLP_MIN);
glp_add_rows(lp, 1 + 3*N*(N-1)/2);
glp_add_cols(lp, N + N*(N-1)/2);
vector<int> ia(1, 0);
vector<int> ja(1, 0);
vector<double> ar(1, 0.0);
for (uint i = 1; i <= N; i++) {
glp_set_obj_coef(lp, i, 0);
glp_set_col_bnds(lp, i, GLP_DB, 0.0, 1.0);
glp_set_col_kind(lp, i, GLP_BV);
}
for (uint i = 1; i <= N; i++) {
ia.push_back(1);
ja.push_back(i);
ar.push_back(1.0);
}
glp_set_row_bnds(lp, 1, GLP_FX, K, K);
// Set:
// 1. coefficient for objective
// 2. row bounds
// 3. column bounds
// 4. coefficient matrix for constraints
uint k = 1;
for (uint i = 1; i <= N-1; i++) {
for (uint j = i+1; j <= N; j++) {
point a = data[i-1];
point b = data[j-1];
// object coefficient and column bounds
double l = handler->loss(handler->dist(a, b));
glp_set_obj_coef(lp, N+k, l);
glp_set_col_bnds(lp, N+k, GLP_DB, 0.0, 1.0);
glp_set_col_kind(lp, N+k, GLP_BV);
// row bounds
glp_set_row_bnds(lp, 1 + (k-1)*3 + 1, GLP_LO, 0.0, 1.0);
glp_set_row_bnds(lp, 1 + (k-1)*3 + 2, GLP_LO, 0.0, 1.0);
glp_set_row_bnds(lp, 1 + (k-1)*3 + 3, GLP_LO, -1.0, 1.0);
// coefficient matrix
ia.push_back(1 + (k-1)*3 + 1);
ja.push_back(i);
ar.push_back(1.0);
ia.push_back(1 + (k-1)*3 + 1);
ja.push_back(N+k);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 2);
ja.push_back(j);
ar.push_back(1.0);
ia.push_back(1 + (k-1)*3 + 2);
ja.push_back(N+k);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 3);
ja.push_back(i);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 3);
ja.push_back(j);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 3);
ja.push_back(N+k);
ar.push_back(1.0);
k++;
}
}
int* ia_a = &ia[0];
int* ja_a = &ja[0];
double* ar_a = &ar[0];
glp_load_matrix(lp, ia.size()-1, ia_a, ja_a, ar_a);
glp_iocp parm;
glp_init_iocp(&parm);
parm.presolve = GLP_ON;
parm.msg_lev = GLP_MSG_OFF;
int ret = glp_intopt(lp, &parm);
// cout << "program returned: " << ret << endl;
// Collect processed results
for (uint i = 1; i <= N; i++) {
double x = glp_mip_col_val(lp, i);
if (x == 1.0) {
sampled.push_back(data[i-1]);
}
}
}