static void parse_objective(struct dsa *dsa)
{ /* parse objective sense */
int k, len;
/* parse the keyword 'minimize' or 'maximize' */
if (dsa->token == T_MINIMIZE)
lpx_set_obj_dir(dsa->lp, LPX_MIN);
else if (dsa->token == T_MAXIMIZE)
lpx_set_obj_dir(dsa->lp, LPX_MAX);
else
xassert(dsa != dsa);
scan_token(dsa);
/* parse objective name */
if (dsa->token == T_NAME && dsa->c == ':')
{ /* objective name is followed by a colon */
lpx_set_obj_name(dsa->lp, dsa->image);
scan_token(dsa);
xassert(dsa->token == T_COLON);
scan_token(dsa);
}
else
{ /* objective name is not specified; use default */
lpx_set_obj_name(dsa->lp, "obj");
}
/* parse linear form */
len = parse_linear_form(dsa);
for (k = 1; k <= len; k++)
lpx_set_obj_coef(dsa->lp, dsa->ind[k], dsa->val[k]);
return;
}
int GLPKLoadObjective(LinEquation* InEquation, bool Max) {
if (InEquation->QuadCoeff.size() > 0) {
FErrorFile() << "GLPK solver cannot accept quadratic objectives." << endl;
FlushErrorFile();
return FAIL;
}
if (GLPKModel == NULL) {
FErrorFile() << "Could not add objective because GLPK object does not exist." << endl;
FlushErrorFile();
return FAIL;
}
if (!Max) {
lpx_set_obj_dir(GLPKModel, LPX_MIN);
} else {
lpx_set_obj_dir(GLPKModel, LPX_MAX);
}
int NumColumns = lpx_get_num_cols(GLPKModel);
for (int i=0; i < NumColumns; i++) {
lpx_set_obj_coef(GLPKModel, i+1, 0);
}
for (int i=0; i < int(InEquation->Variables.size()); i++) {
if (NumColumns > InEquation->Variables[i]->Index) {
lpx_set_obj_coef(GLPKModel, InEquation->Variables[i]->Index+1, InEquation->Coefficient[i]);
} else {
FErrorFile() << "Variable index specified in objective was out of the range of variables added to the GLPK problem object." << endl;
FlushErrorFile();
return FAIL;
}
}
return SUCCESS;
}
int main(void)
{ LPX *lp;
int ia[1+1000], ja[1+1000];
double ar[1+1000], Z, x1, x2, x3;
s1: lp = lpx_create_prob();
s2: lpx_set_prob_name(lp, "sample");
s3: lpx_set_obj_dir(lp, LPX_MAX);
s4: lpx_add_rows(lp, 3);
s5: lpx_set_row_name(lp, 1, "p");
s6: lpx_set_row_bnds(lp, 1, LPX_UP, 0.0, 100.0);
s7: lpx_set_row_name(lp, 2, "q");
s8: lpx_set_row_bnds(lp, 2, LPX_UP, 0.0, 600.0);
s9: lpx_set_row_name(lp, 3, "r");
s10: lpx_set_row_bnds(lp, 3, LPX_UP, 0.0, 300.0);
s11: lpx_add_cols(lp, 3);
s12: lpx_set_col_name(lp, 1, "x1");
s13: lpx_set_col_bnds(lp, 1, LPX_LO, 0.0, 0.0);
s14: lpx_set_obj_coef(lp, 1, 10.0);
s15: lpx_set_col_name(lp, 2, "x2");
s16: lpx_set_col_bnds(lp, 2, LPX_LO, 0.0, 0.0);
s17: lpx_set_obj_coef(lp, 2, 6.0);
s18: lpx_set_col_name(lp, 3, "x3");
s19: lpx_set_col_bnds(lp, 3, LPX_LO, 0.0, 0.0);
s20: lpx_set_obj_coef(lp, 3, 4.0);
s21: ia[1] = 1, ja[1] = 1, ar[1] = 1.0; /* a[1,1] = 1 */
s22: ia[2] = 1, ja[2] = 2, ar[2] = 1.0; /* a[1,2] = 1 */
s23: ia[3] = 1, ja[3] = 3, ar[3] = 1.0; /* a[1,3] = 1 */
s24: ia[4] = 2, ja[4] = 1, ar[4] = 10.0; /* a[2,1] = 10 */
s25: ia[5] = 3, ja[5] = 1, ar[5] = 2.0; /* a[3,1] = 2 */
s26: ia[6] = 2, ja[6] = 2, ar[6] = 4.0; /* a[2,2] = 4 */
s27: ia[7] = 3, ja[7] = 2, ar[7] = 2.0; /* a[3,2] = 2 */
s28: ia[8] = 2, ja[8] = 3, ar[8] = 5.0; /* a[2,3] = 5 */
s29: ia[9] = 3, ja[9] = 3, ar[9] = 6.0; /* a[3,3] = 6 */
s30: lpx_load_matrix(lp, 9, ia, ja, ar);
s31: lpx_simplex(lp);
s32: Z = lpx_get_obj_val(lp);
s33: x1 = lpx_get_col_prim(lp, 1);
s34: x2 = lpx_get_col_prim(lp, 2);
s35: x3 = lpx_get_col_prim(lp, 3);
s36: printf("\nZ = %g; x1 = %g; x2 = %g; x3 = %g\n", Z, x1, x2, x3);
s37: lpx_delete_prob(lp);
return 0;
}
int main(int argc, char *argv[])
{ LPX *lp;
MPL *mpl = NULL;
int ret;
double start;
/* parse command line parameters */
parse_cmdline(argc, argv);
/* remove all output files specified in the command line */
if (display != NULL) remove(display);
if (out_sol != NULL) remove(out_sol);
if (out_bnds != NULL) remove(out_bnds);
if (out_mps != NULL) remove(out_mps);
if (out_lpt != NULL) remove(out_lpt);
if (out_txt != NULL) remove(out_txt);
if (out_glp != NULL) remove(out_glp);
/* read problem from the input file */
if (in_file == NULL)
{ print("No input file specified; try %s --help", argv[0]);
exit(EXIT_FAILURE);
}
switch (format)
{ case 0:
lp = lpx_read_mps(in_file);
if (lp == NULL)
{ print("MPS file processing error");
exit(EXIT_FAILURE);
}
break;
case 1:
lp = lpx_read_lpt(in_file);
if (lp == NULL)
{ print("CPLEX LP file processing error");
exit(EXIT_FAILURE);
}
break;
case 2:
#if 0 /* 01/VIII-2004 */
lp = lpx_read_model(in_file, in_data, display);
if (lp == NULL)
{ print("Model processing error");
exit(EXIT_FAILURE);
}
#else
/* initialize the translator database */
mpl = mpl_initialize();
/* read model section and optional data section */
ret = mpl_read_model(mpl, in_file, in_data != NULL);
if (ret == 4)
err: { print("Model processing error");
exit(EXIT_FAILURE);
}
insist(ret == 1 || ret == 2);
/* read data section, if necessary */
if (in_data != NULL)
{ insist(ret == 1);
ret = mpl_read_data(mpl, in_data);
if (ret == 4) goto err;
insist(ret == 2);
}
/* generate model */
ret = mpl_generate(mpl, display);
if (ret == 4) goto err;
/* extract problem instance */
lp = lpx_extract_prob(mpl);
insist(lp != NULL);
#endif
if (lpx_get_num_rows(lp) == 0)
{ print("Problem has no rows");
exit(EXIT_FAILURE);
}
if (lpx_get_num_cols(lp) == 0)
{ print("Problem has no columns");
exit(EXIT_FAILURE);
}
break;
case 3:
lp = lpx_read_prob(in_file);
if (lp == NULL)
{ print("GNU LP file processing error");
exit(EXIT_FAILURE);
}
break;
default:
insist(format != format);
}
/* change problem name (if required) */
if (newname != NULL) lpx_set_prob_name(lp, newname);
/* change optimization direction (if required) */
if (dir != 0) lpx_set_obj_dir(lp, dir);
/* write problem in MPS format (if required) */
if (out_mps != NULL)
{ lpx_set_int_parm(lp, LPX_K_MPSORIG, orig);
ret = lpx_write_mps(lp, out_mps);
if (ret != 0)
{ print("Unable to write problem in MPS format");
exit(EXIT_FAILURE);
}
}
/* write problem in CPLEX LP format (if required) */
if (out_lpt != NULL)
{ lpx_set_int_parm(lp, LPX_K_LPTORIG, orig);
ret = lpx_write_lpt(lp, out_lpt);
if (ret != 0)
{ print("Unable to write problem in CPLEX LP format");
exit(EXIT_FAILURE);
}
}
/* write problem in plain text format (if required) */
if (out_txt != NULL)
{ lpx_set_int_parm(lp, LPX_K_LPTORIG, orig);
ret = lpx_print_prob(lp, out_txt);
if (ret != 0)
{ print("Unable to write problem in plain text format");
exit(EXIT_FAILURE);
}
}
/* write problem in GNU LP format (if required) */
if (out_glp != NULL)
{ ret = lpx_write_prob(lp, out_glp);
if (ret != 0)
{ print("Unable to write problem in GNU LP format");
exit(EXIT_FAILURE);
}
}
/* if only data check is required, skip computations */
if (check) goto skip;
/* scale the problem data (if required) */
if (scale && (!presol || method == 1)) lpx_scale_prob(lp);
/* build advanced initial basis (if required) */
if (method == 0 && basis && !presol) lpx_adv_basis(lp);
/* set some control parameters, which might be changed in the
command line */
lpx_set_int_parm(lp, LPX_K_PRICE, price);
if (!relax) lpx_set_real_parm(lp, LPX_K_RELAX, 0.0);
lpx_set_int_parm(lp, LPX_K_PRESOL, presol);
lpx_set_int_parm(lp, LPX_K_BRANCH, branch);
lpx_set_int_parm(lp, LPX_K_BTRACK, btrack);
lpx_set_real_parm(lp, LPX_K_TMLIM, (double)tmlim);
/* solve the problem */
start = utime();
switch (method)
{ case 0:
if (nomip || lpx_get_class(lp) == LPX_LP)
{ ret = lpx_simplex(lp);
if (presol && ret != LPX_E_OK && out_sol != NULL)
print("If you need actual output for non-optimal solu"
"tion, use --nopresol");
}
else
{ method = 2;
lpx_simplex(lp);
if (!intopt)
lpx_integer(lp);
else
lpx_intopt(lp);
}
break;
case 1:
if (nomip || lpx_get_class(lp) == LPX_LP)
lpx_interior(lp);
else
{ print("Interior point method is not able to solve MIP pr"
"oblem; use --simplex");
exit(EXIT_FAILURE);
}
break;
default:
insist(method != method);
}
/* display statistics */
print("Time used: %.1f secs", utime() - start);
print("Memory used: %.1fM (%d bytes)",
(double)lib_env_ptr()->mem_tpeak / (double)(1024 * 1024),
lib_env_ptr()->mem_tpeak);
#if 1 /* 01/VIII-2004 */
if (mpl != NULL && mpl_has_solve_stmt(mpl))
{ int n, j, round;
/* store the solution to the translator database */
n = lpx_get_num_cols(lp);
round = lpx_get_int_parm(lp, LPX_K_ROUND);
lpx_set_int_parm(lp, LPX_K_ROUND, 1);
switch (method)
{ case 0:
for (j = 1; j <= n; j++)
mpl_put_col_value(mpl, j, lpx_get_col_prim(lp, j));
break;
case 1:
for (j = 1; j <= n; j++)
mpl_put_col_value(mpl, j, lpx_ipt_col_prim(lp, j));
break;
case 2:
for (j = 1; j <= n; j++)
mpl_put_col_value(mpl, j, lpx_mip_col_val(lp, j));
break;
default:
insist(method != method);
}
lpx_set_int_parm(lp, LPX_K_ROUND, round);
/* perform postsolving */
ret = mpl_postsolve(mpl, display);
if (ret == 4)
{ print("Model postsolving error");
exit(EXIT_FAILURE);
}
insist(ret == 3);
}
#endif
/* write problem solution found by the solver (if required) */
if (out_sol != NULL)
{ switch (method)
{ case 0:
ret = lpx_print_sol(lp, out_sol);
break;
case 1:
ret = lpx_print_ips(lp, out_sol);
break;
case 2:
ret = lpx_print_mip(lp, out_sol);
break;
default:
insist(method != method);
}
if (ret != 0)
{ print("Unable to write problem solution");
exit(EXIT_FAILURE);
}
}
/* write sensitivity bounds information (if required) */
if (out_bnds != NULL)
{ if (method != 0)
{ print("Cannot write sensitivity bounds information for inte"
"rior-point or MIP solution");
exit(EXIT_FAILURE);
}
ret = lpx_print_sens_bnds(lp, out_bnds);
if (ret != 0)
{ print("Unable to write sensitivity bounds information");
exit(EXIT_FAILURE);
}
}
skip: /* delete the problem object */
lpx_delete_prob(lp);
#if 1 /* 01/VIII-2004 */
/* if the translator database exists, destroy it */
if (mpl != NULL) mpl_terminate(mpl);
#endif
/* check that no memory blocks are still allocated */
insist(lib_env_ptr()->mem_total == 0);
insist(lib_env_ptr()->mem_count == 0);
/* return to the control program */
return 0;
}
LPX *lpp_build_prob(LPP *lpp)
{ LPX *prob;
LPPROW *row;
LPPCOL *col;
struct load_info info;
int i, j, typx;
/* count number of rows and columns in the resultant problem */
lpp->m = lpp->n = 0;
for (row = lpp->row_ptr; row != NULL; row = row->next) lpp->m++;
for (col = lpp->col_ptr; col != NULL; col = col->next) lpp->n++;
/* allocate two arrays to save reference numbers assigned to rows
and columns of the resultant problem */
lpp->row_ref = ucalloc(1+lpp->m, sizeof(int));
lpp->col_ref = ucalloc(1+lpp->n, sizeof(int));
/* create LP problem object */
prob = lpx_create_prob();
/* the resultant problem should have the same optimization sense
as the original problem */
lpx_set_obj_dir(prob, lpp->orig_dir);
/* set the constant term of the objective function */
lpx_set_obj_c0(prob,
lpp->orig_dir == LPX_MIN ? + lpp->c0 : - lpp->c0);
/* create rows of the resultant problem */
insist(lpp->m > 0);
lpx_add_rows(prob, lpp->m);
for (i = 1, row = lpp->row_ptr; i <= lpp->m; i++, row = row->next)
{ insist(row != NULL);
lpp->row_ref[i] = row->i;
row->i = i;
if (row->lb == -DBL_MAX && row->ub == +DBL_MAX)
typx = LPX_FR;
else if (row->ub == +DBL_MAX)
typx = LPX_LO;
else if (row->lb == -DBL_MAX)
typx = LPX_UP;
else if (row->lb != row->ub)
typx = LPX_DB;
else
typx = LPX_FX;
lpx_set_row_bnds(prob, i, typx, row->lb, row->ub);
}
insist(row == NULL);
/* create columns of the resultant problem */
insist(lpp->n > 0);
lpx_add_cols(prob, lpp->n);
for (j = 1, col = lpp->col_ptr; j <= lpp->n; j++, col = col->next)
{ insist(col != NULL);
lpp->col_ref[j] = col->j;
col->j = j;
if (col->lb == -DBL_MAX && col->ub == +DBL_MAX)
typx = LPX_FR;
else if (col->ub == +DBL_MAX)
typx = LPX_LO;
else if (col->lb == -DBL_MAX)
typx = LPX_UP;
else if (col->lb != col->ub)
typx = LPX_DB;
else
typx = LPX_FX;
lpx_set_col_bnds(prob, j, typx, col->lb, col->ub);
lpx_set_col_coef(prob, j,
lpp->orig_dir == LPX_MIN ? + col->c : - col->c);
}
insist(col == NULL);
/* create the constraint matrix of the resultant problem */
info.lpp = lpp;
info.row = NULL;
info.aij = NULL;
lpx_load_mat(prob, &info, next_aij);
/* count number of non-zeros in the resultant problem */
lpp->nnz = lpx_get_num_nz(prob);
/* internal data structures that represnts the resultant problem
are no longer needed, so free them */
dmp_delete_pool(lpp->row_pool), lpp->row_pool = NULL;
dmp_delete_pool(lpp->col_pool), lpp->col_pool = NULL;
dmp_delete_pool(lpp->aij_pool), lpp->aij_pool = NULL;
lpp->row_ptr = NULL, lpp->col_ptr = NULL;
lpp->row_que = NULL, lpp->col_que = NULL;
/* return a pointer to the built LP problem object */
return prob;
}
int main(int argc, char *argv[])
{ LPX *lp;
MPL *mpl = NULL;
int ret;
ulong_t start;
/* parse command line parameters */
parse_cmdline(argc, argv);
/* set available memory limit */
if (memlim >= 0)
lib_mem_limit(ulmul(ulset(0, 1048576), ulset(0, memlim)));
/* remove all output files specified in the command line */
if (display != NULL) remove(display);
if (out_bas != NULL) remove(out_bas);
if (out_sol != NULL) remove(out_sol);
if (out_bnds != NULL) remove(out_bnds);
if (out_mps != NULL) remove(out_mps);
if (out_freemps != NULL) remove(out_freemps);
if (out_cpxlp != NULL) remove(out_cpxlp);
if (out_txt != NULL) remove(out_txt);
if (out_glp != NULL) remove(out_glp);
if (log_file != NULL) remove(log_file);
/* open hardcopy file, if necessary */
if (log_file != NULL)
{ if (lib_open_log(log_file))
{ print("Unable to create log file");
exit(EXIT_FAILURE);
}
}
/* read problem data from the input file */
if (in_file == NULL)
{ print("No input file specified; try %s --help", argv[0]);
exit(EXIT_FAILURE);
}
switch (format)
{ case 0:
lp = lpx_read_mps(in_file);
if (lp == NULL)
{ print("MPS file processing error");
exit(EXIT_FAILURE);
}
orig = 1;
break;
case 1:
lp = lpx_read_cpxlp(in_file);
if (lp == NULL)
{ print("CPLEX LP file processing error");
exit(EXIT_FAILURE);
}
break;
case 2:
/* initialize the translator database */
mpl = mpl_initialize();
/* read model section and optional data section */
ret = mpl_read_model(mpl, in_file, in_data != NULL);
if (ret == 4)
err: { print("Model processing error");
exit(EXIT_FAILURE);
}
xassert(ret == 1 || ret == 2);
/* read data section, if necessary */
if (in_data != NULL)
{ xassert(ret == 1);
ret = mpl_read_data(mpl, in_data);
if (ret == 4) goto err;
xassert(ret == 2);
}
/* generate model */
ret = mpl_generate(mpl, display);
if (ret == 4) goto err;
/* extract problem instance */
lp = lpx_extract_prob(mpl);
xassert(lp != NULL);
break;
case 3:
lp = lpx_read_prob(in_file);
if (lp == NULL)
{ print("GNU LP file processing error");
exit(EXIT_FAILURE);
}
break;
case 4:
lp = lpx_read_freemps(in_file);
if (lp == NULL)
{ print("MPS file processing error");
exit(EXIT_FAILURE);
}
break;
default:
xassert(format != format);
}
/* order rows and columns of the constraint matrix */
lpx_order_matrix(lp);
/* change problem name (if required) */
if (newname != NULL) lpx_set_prob_name(lp, newname);
/* change optimization direction (if required) */
if (dir != 0) lpx_set_obj_dir(lp, dir);
/* write problem in fixed MPS format (if required) */
if (out_mps != NULL)
{ lpx_set_int_parm(lp, LPX_K_MPSORIG, orig);
ret = lpx_write_mps(lp, out_mps);
if (ret != 0)
{ print("Unable to write problem in fixed MPS format");
exit(EXIT_FAILURE);
}
}
/* write problem in free MPS format (if required) */
if (out_freemps != NULL)
{ ret = lpx_write_freemps(lp, out_freemps);
if (ret != 0)
{ print("Unable to write problem in free MPS format");
exit(EXIT_FAILURE);
}
}
/* write problem in CPLEX LP format (if required) */
if (out_cpxlp != NULL)
{ ret = lpx_write_cpxlp(lp, out_cpxlp);
if (ret != 0)
{ print("Unable to write problem in CPLEX LP format");
exit(EXIT_FAILURE);
}
}
/* write problem in plain text format (if required) */
if (out_txt != NULL)
{ lpx_set_int_parm(lp, LPX_K_LPTORIG, orig);
ret = lpx_print_prob(lp, out_txt);
if (ret != 0)
{ print("Unable to write problem in plain text format");
exit(EXIT_FAILURE);
}
}
/* write problem in GNU LP format (if required) */
if (out_glp != NULL)
{ ret = lpx_write_prob(lp, out_glp);
if (ret != 0)
{ print("Unable to write problem in GNU LP format");
exit(EXIT_FAILURE);
}
}
/* if only data check is required, skip computations */
if (check) goto skip;
/* scale the problem data (if required) */
if (scale && (!presol || method == 1)) lpx_scale_prob(lp);
/* build initial LP basis */
if (method == 0 && !presol && in_bas == NULL)
{ switch (basis)
{ case 0:
lpx_std_basis(lp);
break;
case 1:
if (lpx_get_num_rows(lp) > 0 && lpx_get_num_cols(lp) > 0)
lpx_adv_basis(lp);
break;
case 2:
if (lpx_get_num_rows(lp) > 0 && lpx_get_num_cols(lp) > 0)
lpx_cpx_basis(lp);
break;
default:
xassert(basis != basis);
}
}
/* or read initial basis from input text file in MPS format */
if (in_bas != NULL)
{ if (method != 0)
{ print("Initial LP basis is useless for interior-point solve"
"r and therefore ignored");
goto nobs;
}
lpx_set_int_parm(lp, LPX_K_MPSORIG, orig);
ret = lpx_read_bas(lp, in_bas);
if (ret != 0)
{ print("Unable to read initial LP basis");
exit(EXIT_FAILURE);
}
if (presol)
{ presol = 0;
print("LP presolver disabled because initial LP basis has b"
"een provided");
}
nobs: ;
}
/* set some control parameters, which might be changed in the
command line */
lpx_set_int_parm(lp, LPX_K_BFTYPE, bf_type);
lpx_set_int_parm(lp, LPX_K_PRICE, price);
if (!relax) lpx_set_real_parm(lp, LPX_K_RELAX, 0.0);
lpx_set_int_parm(lp, LPX_K_PRESOL, presol);
lpx_set_int_parm(lp, LPX_K_BRANCH, branch);
lpx_set_int_parm(lp, LPX_K_BTRACK, btrack);
lpx_set_real_parm(lp, LPX_K_TMLIM, (double)tmlim);
lpx_set_int_parm(lp, LPX_K_BINARIZE, binarize);
lpx_set_int_parm(lp, LPX_K_USECUTS, use_cuts);
/* solve the problem */
start = xtime();
switch (method)
{ case 0:
if (nomip || lpx_get_class(lp) == LPX_LP)
{ ret = (!exact ? lpx_simplex(lp) : lpx_exact(lp));
if (xcheck)
{ if (!presol || ret == LPX_E_OK)
lpx_exact(lp);
else
print("If you need checking final basis for non-op"
"timal solution, use --nopresol");
}
if (presol && ret != LPX_E_OK && (out_bas != NULL ||
out_sol != NULL))
print("If you need actual output for non-optimal solu"
"tion, use --nopresol");
}
else
{ method = 2;
if (!intopt)
{ ret = (!exact ? lpx_simplex(lp) : lpx_exact(lp));
if (xcheck && (!presol || ret == LPX_E_OK))
lpx_exact(lp);
lpx_integer(lp);
}
else
lpx_intopt(lp);
}
break;
case 1:
if (nomip || lpx_get_class(lp) == LPX_LP)
lpx_interior(lp);
else
{ print("Interior-point method is not able to solve MIP pr"
"oblem; use --simplex");
exit(EXIT_FAILURE);
}
break;
default:
xassert(method != method);
}
/* display statistics */
print("Time used: %.1f secs", xdifftime(xtime(), start));
{ ulong_t tpeak;
char buf[50];
lib_mem_usage(NULL, NULL, NULL, &tpeak);
print("Memory used: %.1f Mb (%s bytes)",
(4294967296.0 * tpeak.hi + tpeak.lo) / 1048576.0,
ultoa(tpeak, buf, 10));
}
if (mpl != NULL && mpl_has_solve_stmt(mpl))
{ int n, j, round;
/* store the solution to the translator database */
n = lpx_get_num_cols(lp);
round = lpx_get_int_parm(lp, LPX_K_ROUND);
lpx_set_int_parm(lp, LPX_K_ROUND, 1);
switch (method)
{ case 0:
for (j = 1; j <= n; j++)
mpl_put_col_value(mpl, j, lpx_get_col_prim(lp, j));
break;
case 1:
for (j = 1; j <= n; j++)
mpl_put_col_value(mpl, j, lpx_ipt_col_prim(lp, j));
break;
case 2:
for (j = 1; j <= n; j++)
mpl_put_col_value(mpl, j, lpx_mip_col_val(lp, j));
break;
default:
xassert(method != method);
}
lpx_set_int_parm(lp, LPX_K_ROUND, round);
/* perform postsolving */
ret = mpl_postsolve(mpl);
if (ret == 4)
{ print("Model postsolving error");
exit(EXIT_FAILURE);
}
xassert(ret == 3);
}
/* write final LP basis (if required) */
if (out_bas != NULL)
{ lpx_set_int_parm(lp, LPX_K_MPSORIG, orig);
ret = lpx_write_bas(lp, out_bas);
if (ret != 0)
{ print("Unable to write final LP basis");
exit(EXIT_FAILURE);
}
}
/* write problem solution found by the solver (if required) */
if (out_sol != NULL)
{ switch (method)
{ case 0:
ret = lpx_print_sol(lp, out_sol);
break;
case 1:
ret = lpx_print_ips(lp, out_sol);
break;
case 2:
ret = lpx_print_mip(lp, out_sol);
break;
default:
xassert(method != method);
}
if (ret != 0)
{ print("Unable to write problem solution");
exit(EXIT_FAILURE);
}
}
/* write sensitivity bounds information (if required) */
if (out_bnds != NULL)
{ if (method != 0)
{ print("Cannot write sensitivity bounds information for inte"
"rior-point or MIP solution");
exit(EXIT_FAILURE);
}
ret = lpx_print_sens_bnds(lp, out_bnds);
if (ret != 0)
{ print("Unable to write sensitivity bounds information");
exit(EXIT_FAILURE);
}
}
skip: /* delete the problem object */
lpx_delete_prob(lp);
/* if the translator database exists, destroy it */
if (mpl != NULL) mpl_terminate(mpl);
xassert(gmp_pool_count() == 0);
gmp_free_mem();
/* close the hardcopy file */
if (log_file != NULL) lib_close_log();
/* check that no memory blocks are still allocated */
{ int count;
ulong_t total;
lib_mem_usage(&count, NULL, &total, NULL);
xassert(count == 0);
xassert(total.lo == 0 && total.hi == 0);
}
/* free the library environment */
lib_free_env();
/* return to the control program */
return 0;
}
int CClp_loadlp(CClp *lp, const char *name, int ncols, int nrows,
int objsense, double *obj, double *rhs, char *sense, int *matbeg,
int *matcnt, int *matind, double *matval, double *lb, double *ub)
{ /* LOADS the data into the LP.
- name attaches a name to the LP (it can be used by the LP
solver in io routines)
- ncols and nrows give the number of columns and rows in the LP
- objsense should be 1 for minimize and -1 for maximize
- obj and rhs are arrays giving the objective function and rhs
- sense is an array specifying 'L', 'E', or 'G' for each of the
rows
- matbeg, matcnt, matind, and matval give the coefficients of
the constraint matrix in column by column order.
matbeg gives the index of the start of each column;
matcnt gives the number of coefficients in each column;
matind gives the indices of the rows where the coefficients
are located in the constraint matrix (so for column j, the
indices are given in matcnt[j] locations starting at
matind[matbeg[j]]; and matval gives the actual coefficients
(organized like matind).
- lb and ub are arrays giving the upper and lower bounds of the
variables. */
int i, j;
/* create empty problem object */
insist(lp->lp == NULL);
lp->lp = lpx_create_prob();
lpx_set_prob_name(lp->lp, (char *)name);
/* set objective sense */
switch (objsense)
{ case +1:
/* minimization */
lpx_set_obj_dir(lp->lp, LPX_MIN); break;
case -1:
/* maximization */
lpx_set_obj_dir(lp->lp, LPX_MAX); break;
default:
insist(objsense != objsense);
}
/* add rows */
lpx_add_rows(lp->lp, nrows);
for (i = 0; i < nrows; i++)
{ int seqn, type;
double lo, up;
seqn = i+1;
switch (sense[i])
{ case 'L':
type = LPX_UP, lo = 0.0, up = rhs[i];
break;
case 'E':
type = LPX_FX, lo = up = rhs[i];
break;
case 'G':
type = LPX_LO, lo = rhs[i], up = 0.0;
break;
default:
insist(sense[i] != sense[i]);
}
lpx_set_row_bnds(lp->lp, seqn, type, lo, up);
}
/* add columns and constraint coefficients */
lpx_add_cols(lp->lp, ncols);
for (j = 0; j < ncols; j++)
{ int seqn, type, k;
double lo, up;
seqn = j+1;
lpx_set_col_coef(lp->lp, seqn, obj == NULL ? 0.0 : obj[j]);
lo = lb[j], up = ub[j];
/* check for finite bounds */
insist(-1e12 <= lo && lo <= up && up <= +1e12);
type = (lo == up ? LPX_FX : LPX_DB);
lpx_set_col_bnds(lp->lp, seqn, type, lo, up);
for (k = matbeg[j]; k < matbeg[j] + matcnt[j]; k++)
matind[k]++;
lpx_set_mat_col(lp->lp, seqn, matcnt[j],
&matind[matbeg[j]] - 1, &matval[matbeg[j]] - 1);
for (k = matbeg[j]; k < matbeg[j] + matcnt[j]; k++)
matind[k]--;
}
return 0;
}
LPX *lpx_extract_prob(void *_mpl)
{ MPL *mpl = _mpl;
LPX *lp;
int m, n, i, j, t, kind, type, len, *ind;
double lb, ub, *val;
/* create problem instance */
lp = lpx_create_prob();
/* set problem name */
lpx_set_prob_name(lp, mpl_get_prob_name(mpl));
/* build rows (constraints) */
m = mpl_get_num_rows(mpl);
if (m > 0) lpx_add_rows(lp, m);
for (i = 1; i <= m; i++)
{ /* set row name */
lpx_set_row_name(lp, i, mpl_get_row_name(mpl, i));
/* set row bounds */
type = mpl_get_row_bnds(mpl, i, &lb, &ub);
switch (type)
{ case MPL_FR: type = LPX_FR; break;
case MPL_LO: type = LPX_LO; break;
case MPL_UP: type = LPX_UP; break;
case MPL_DB: type = LPX_DB; break;
case MPL_FX: type = LPX_FX; break;
default: insist(type != type);
}
if (type == LPX_DB && fabs(lb - ub) < 1e-9 * (1.0 + fabs(lb)))
{ type = LPX_FX;
if (fabs(lb) <= fabs(ub)) ub = lb; else lb = ub;
}
lpx_set_row_bnds(lp, i, type, lb, ub);
/* warn about non-zero constant term */
if (mpl_get_row_c0(mpl, i) != 0.0)
print("lpx_read_model: row %s; constant term %.12g ignored",
mpl_get_row_name(mpl, i), mpl_get_row_c0(mpl, i));
}
/* build columns (variables) */
n = mpl_get_num_cols(mpl);
if (n > 0) lpx_add_cols(lp, n);
for (j = 1; j <= n; j++)
{ /* set column name */
lpx_set_col_name(lp, j, mpl_get_col_name(mpl, j));
/* set column kind */
kind = mpl_get_col_kind(mpl, j);
switch (kind)
{ case MPL_NUM:
break;
case MPL_INT:
case MPL_BIN:
lpx_set_class(lp, LPX_MIP);
lpx_set_col_kind(lp, j, LPX_IV);
break;
default:
insist(kind != kind);
}
/* set column bounds */
type = mpl_get_col_bnds(mpl, j, &lb, &ub);
switch (type)
{ case MPL_FR: type = LPX_FR; break;
case MPL_LO: type = LPX_LO; break;
case MPL_UP: type = LPX_UP; break;
case MPL_DB: type = LPX_DB; break;
case MPL_FX: type = LPX_FX; break;
default: insist(type != type);
}
if (kind == MPL_BIN)
{ if (type == LPX_FR || type == LPX_UP || lb < 0.0) lb = 0.0;
if (type == LPX_FR || type == LPX_LO || ub > 1.0) ub = 1.0;
type = LPX_DB;
}
if (type == LPX_DB && fabs(lb - ub) < 1e-9 * (1.0 + fabs(lb)))
{ type = LPX_FX;
if (fabs(lb) <= fabs(ub)) ub = lb; else lb = ub;
}
lpx_set_col_bnds(lp, j, type, lb, ub);
}
/* load the constraint matrix */
ind = ucalloc(1+n, sizeof(int));
val = ucalloc(1+n, sizeof(double));
for (i = 1; i <= m; i++)
{ len = mpl_get_mat_row(mpl, i, ind, val);
lpx_set_mat_row(lp, i, len, ind, val);
}
/* build objective function (the first objective is used) */
for (i = 1; i <= m; i++)
{ kind = mpl_get_row_kind(mpl, i);
if (kind == MPL_MIN || kind == MPL_MAX)
{ /* set objective name */
lpx_set_obj_name(lp, mpl_get_row_name(mpl, i));
/* set optimization direction */
lpx_set_obj_dir(lp, kind == MPL_MIN ? LPX_MIN : LPX_MAX);
/* set constant term */
lpx_set_obj_coef(lp, 0, mpl_get_row_c0(mpl, i));
/* set objective coefficients */
len = mpl_get_mat_row(mpl, i, ind, val);
for (t = 1; t <= len; t++)
lpx_set_obj_coef(lp, ind[t], val[t]);
break;
}
}
/* free working arrays */
ufree(ind);
ufree(val);
/* bring the problem object to the calling program */
return lp;
}
LPX *ipp_build_prob(IPP *ipp)
{ LPX *prob;
IPPROW *row;
IPPCOL *col;
IPPAIJ *aij;
int i, j, type, len, *ind;
double *val;
/* create problem object */
prob = lpx_create_prob();
#if 0
lpx_set_class(prob, LPX_MIP);
#endif
/* the resultant problem should have the same optimization sense
as the original problem */
lpx_set_obj_dir(prob, ipp->orig_dir);
/* set the constant term of the objective function */
lpx_set_obj_coef(prob, 0,
ipp->orig_dir == LPX_MIN ? + ipp->c0 : - ipp->c0);
/* copy rows of the resultant problem */
for (row = ipp->row_ptr; row != NULL; row = row->next)
{ i = lpx_add_rows(prob, 1);
if (row->lb == -DBL_MAX && row->ub == +DBL_MAX)
type = LPX_FR;
else if (row->ub == +DBL_MAX)
type = LPX_LO;
else if (row->lb == -DBL_MAX)
type = LPX_UP;
else if (row->lb != row->ub)
type = LPX_DB;
else
type = LPX_FX;
lpx_set_row_bnds(prob, i, type, row->lb, row->ub);
row->temp = i;
}
/* copy columns of the resultant problem */
ind = xcalloc(1+lpx_get_num_rows(prob), sizeof(int));
val = xcalloc(1+lpx_get_num_rows(prob), sizeof(double));
for (col = ipp->col_ptr; col != NULL; col = col->next)
{ j = lpx_add_cols(prob, 1);
if (col->i_flag) lpx_set_col_kind(prob, j, LPX_IV);
if (col->lb == -DBL_MAX && col->ub == +DBL_MAX)
type = LPX_FR;
else if (col->ub == +DBL_MAX)
type = LPX_LO;
else if (col->lb == -DBL_MAX)
type = LPX_UP;
else if (col->lb != col->ub)
type = LPX_DB;
else
type = LPX_FX;
lpx_set_col_bnds(prob, j, type, col->lb, col->ub);
lpx_set_obj_coef(prob, j,
ipp->orig_dir == LPX_MIN ? + col->c : - col->c);
/* copy constraint coefficients */
len = 0;
for (aij = col->ptr; aij != NULL; aij = aij->c_next)
{ len++;
ind[len] = aij->row->temp;
val[len] = aij->val;
}
lpx_set_mat_col(prob, j, len, ind, val);
}
xfree(ind);
xfree(val);
return prob;
}
int TankBlendOptimiser::go()
{
lp = lpx_create_prob();
lpx_set_int_parm(lp, LPX_K_MSGLEV, 0); // 0 = no output
lpx_set_int_parm(lp, LPX_K_SCALE, 3); // 3 = geometric mean scaling, then equilibration scaling
lpx_set_int_parm(lp, LPX_K_DUAL, 1); // 1 = if initial basic solution is dual feasible, use the dual simplex
lpx_set_int_parm(lp, LPX_K_ROUND, 1); // 1 = replace tiny primal and dual values by exact zero
lpx_set_int_parm(lp, LPX_K_PRESOL, 1); // 1 = use the built-in presolver.
lpx_set_prob_name(lp, "Blend Optimiser");
lpx_set_obj_dir(lp, LPX_MIN);
// Columns...
lpx_add_cols(lp, cols);
for (int i=0; i<tanks; i++) // 0.0 < x < tankMax
{
if (tankMax[i]>0.0)
lpx_set_col_bnds(lp, col, LPX_DB, 0.0, tankMax[i]);
else
lpx_set_col_bnds(lp, col, LPX_FX, 0.0, 0.0);
col++;
}
for (int i=0; i<tanks; i++) // 0.0 < slackTankLow
{
lpx_set_col_bnds(lp, col, LPX_LO, 0.0, 0.0);
lpx_set_obj_coef(lp, col, tankLowPenalty[i]); // penalty weight.
col++;
}
for (int i=0; i<tanks; i++) // 0.0 < slackTankHigh
{
lpx_set_col_bnds(lp, col, LPX_LO, 0.0, 0.0);
lpx_set_obj_coef(lp, col, tankHighPenalty[i]); // penalty weight.
col++;
}
for (int i=0; i<assays; i++) // 0.0 < slackAssayLow
{
lpx_set_col_bnds(lp, col, LPX_LO, 0.0, 0.0);
lpx_set_obj_coef(lp, col, assayLowPenalty[i]); // penalty weight.
col++;
}
for (int i=0; i<assays; i++) // 0.0 < slackAssayHigh
{
lpx_set_col_bnds(lp, col, LPX_LO, 0.0, 0.0);
lpx_set_obj_coef(lp, col, assayHighPenalty[i]); // penalty weight.
col++;
}
for (int i=0; i<assays; i++) // 0.0 < slackAssayRatioLow
for (int j=0; j<assays; j++)
{
lpx_set_col_bnds(lp, col, LPX_LO, 0.0, 0.0);
lpx_set_obj_coef(lp, col, assayRatioLowPenalty[i][j]); // penalty weight.
col++;
}
for (int i=0; i<assays; i++) // 0.0 < slackAssayRatioHigh
for (int j=0; j<assays; j++)
{
lpx_set_col_bnds(lp, col, LPX_LO, 0.0, 0.0);
lpx_set_obj_coef(lp, col, assayRatioHighPenalty[i][j]); // penalty weight.
col++;
}
// Rows...
lpx_add_rows(lp, rows);
{ // x1 + ... + xn = 1.0
lpx_set_row_bnds(lp, row, LPX_FX, 1.0, 1.0);
for (int i=0; i<tanks; i++)
ia[constraint] = row, ja[constraint] = 1+i, ar[constraint++] = 1.0;
row++;
}
for (int i=0; i<tanks; i++) // tankLow < tank + slackTankLow
{
lpx_set_row_bnds(lp, row, LPX_LO, tankLow[i], 0.0);
ia[constraint] = row, ja[constraint] = 1+i, ar[constraint++] = 1.0;
ia[constraint] = row, ja[constraint] = 1+i+tanks, ar[constraint++] = 1.0;
row++;
}
for (int i=0; i<tanks; i++) // tank - slackTankHigh < tankHigh
{
lpx_set_row_bnds(lp, row, LPX_UP, 0.0, tankHigh[i]);
ia[constraint] = row, ja[constraint] = 1+i, ar[constraint++] = 1.0;
ia[constraint] = row, ja[constraint] = 1+i+2*tanks, ar[constraint++] = -1.0;
row++;
}
for (int i=0; i<assays; i++) // assayLow < assay + slackAssayLow
{
lpx_set_row_bnds(lp, row, LPX_LO, assayLow[i], 0.0);
for (int j=0; j<tanks; j++)
{
if (assayConc[i][j]>0.0)
ia[constraint] = row, ja[constraint] = 1+j, ar[constraint++] = assayConc[i][j];
}
ia[constraint] = row, ja[constraint] = 1+i+3*tanks, ar[constraint++] = 1.0;
row++;
}
for (int i=0; i<assays; i++) // assay - slackAssayHigh < assayHigh
{
lpx_set_row_bnds(lp, row, LPX_UP, 0.0, assayHigh[i]);
for (int j=0; j<tanks; j++)
{
if (assayConc[i][j]>0.0)
ia[constraint] = row, ja[constraint] = 1+j, ar[constraint++] = assayConc[i][j];
}
ia[constraint] = row, ja[constraint] = 1+i+3*tanks+assays, ar[constraint++] = -1.0;
row++;
}
for (int i=0; i<assays; i++) // 0 < assayNum - assayRatioLow*assayDen + slackAssayLow
for (int j=0; j<assays; j++)
{
if (assayRatioLowEnabled[i][j])
lpx_set_row_bnds(lp, row, LPX_LO, 0.0, 0.0);
else
lpx_set_row_bnds(lp, row, LPX_FR, 0.0, 0.0);
for (int k=0; k<tanks; k++)
{
if (assayConc[i][k] - assayRatioLow[i][j]*assayConc[j][k]!=0.0)
ia[constraint] = row, ja[constraint] = 1+k, ar[constraint++] = assayConc[i][k] - assayRatioLow[i][j]*assayConc[j][k];
}
ia[constraint] = row, ja[constraint] = 1+i*assays+j+3*tanks+2*assays, ar[constraint++] = 1.0;
row++;
}
for (int i=0; i<assays; i++) // assayNum - assayRatioHigh*assayDen - slackAssayHigh < 0
for (int j=0; j<assays; j++)
{
if (assayRatioHighEnabled[i][j])
lpx_set_row_bnds(lp, row, LPX_UP, 0.0, 0.0);
else
lpx_set_row_bnds(lp, row, LPX_FR, 0.0, 0.0);
for (int k=0; k<tanks; k++)
{
if (assayConc[i][k] - assayRatioHigh[i][j]*assayConc[j][k]!=0.0)
ia[constraint] = row, ja[constraint] = 1+k, ar[constraint++] = assayConc[i][k] - assayRatioHigh[i][j]*assayConc[j][k];
}
ia[constraint] = row, ja[constraint] = 1+i*assays+j+3*tanks+2*assays+assays*assays, ar[constraint++] = -1.0;
row++;
}
lpx_load_matrix(lp, constraint-1, ia, ja, ar);
int exitCode = lpx_simplex(lp);
for (int i=0; i<tanks; i++)
tank[i] = lpx_get_col_prim(lp, 1+i);
for (int i=0; i<assays; i++)
{
assay[i] = 0.0;
for (int j=0; j<tanks; j++)
assay[i] += lpx_get_col_prim(lp, 1+j)*assayConc[i][j];
}
return exitCode;
// LPX_E_OK 200 /* success */
// LPX_E_FAULT 204 /* unable to start the search */
// LPX_E_ITLIM 207 /* iterations limit exhausted */
// LPX_E_TMLIM 208 /* time limit exhausted */
// LPX_E_SING 211 /* problems with basis matrix */
// LPX_E_NOPFS 213 /* no primal feas. sol. (LP presolver) */
// LPX_E_NODFS 214 /* no dual feas. sol. (LP presolver) */
// Usually:
// LPX_E_OK = Solution found.
// LPX_E_NOPFS = Sum-to-1.0 or tank-max constraints not met.
// Others = Some major fault has occurred.
}
void Gspan::lpboost(){
std::cout << "in lpboost" << std::endl;
const char *out = "model";
//initialize
unsigned int gnum = gdata.size();
weight.resize(gnum);
std::fill(weight.begin(),weight.end(),1.0);
corlab.resize(gnum);
for(unsigned int gid=0;gid<gnum;++gid){
corlab[gid]=gdata[gid].class_label;
}
wbias=0.0;
Hypothesis model;
first_flag=true;
need_to_cooc = false;
cooc_is_opt = false;
std::cout.setf(std::ios::fixed,std::ios::floatfield);
std::cout.precision(8);
//Initialize GLPK
int* index = new int[gnum+2]; double* value = new double[gnum+2];
LPX* lp = lpx_create_prob();
lpx_add_cols(lp, gnum+1); // set u_1,...u_l, beta
for (unsigned int i = 0; i < gnum; ++i){
lpx_set_col_bnds(lp, COL(i), LPX_DB, 0.0, 1/(nu*gnum));
lpx_set_obj_coef(lp, COL(i), 0); // u
}
lpx_set_col_bnds(lp, COL(gnum), LPX_FR, 0.0, 0.0);
lpx_set_obj_coef(lp, COL(gnum), 1); // beta
lpx_set_obj_dir(lp, LPX_MIN); //optimization direction: min objective
lpx_add_rows(lp,1); // Add one row constraint s.t. sum_u == 1
for (unsigned int i = 0; i < gnum; ++i){
index[i+1] = COL(i);
value[i+1] = 1;
}
lpx_set_mat_row(lp, ROW(0), gnum, index, value);
lpx_set_row_bnds(lp, ROW(0), LPX_FX, 1, 1);
double beta = 0.0;
double margin = 0.0;
//main loop
for(unsigned int itr=0;itr < max_itr;++itr){
std::cout <<"itrator : "<<itr+1<<std::endl;
if(itr==coocitr) need_to_cooc=true;
opt_pat.gain=0.0;//gain init
opt_pat.size=0;
opt_pat.locsup.resize(0);
pattern.resize(0);
opt_pat.dfscode="";
Crun();
//std::cout<<opt_pat.gain<<" :"<<opt_pat.dfscode<<std::endl;
std::vector <int> result (gnum);
int _y;
vector<int> locvec;
std::string dfscode;
if(cooc_is_opt == false){
_y = opt_pat.gain > 0 ? +1 :-1;
locvec =opt_pat.locsup;
dfscode=opt_pat.dfscode;
}else{
_y = opt_pat_cooc.gain > 0 ? +1 :-1;
locvec =opt_pat_cooc.locsup;
dfscode=opt_pat_cooc.dfscode[0]+"\t"+opt_pat_cooc.dfscode[1];//=opt_pat_cooc.dfscode;
}
model.flag.resize(itr+1);
model.flag[itr]=_y;
std::fill (result.begin (), result.end(), -_y);
for (unsigned int i = 0; i < locvec.size(); ++i) result[locvec[i]] = _y;
double uyh = 0;
for (unsigned int i = 0; i < gnum; ++i) { // summarizing hypotheses
uyh += weight[i]*corlab[i]*result[i];
}
std::cout << "Stopping criterion: " << uyh << "<=?" << beta << " + " << conv_epsilon << std::endl;
if( (uyh <= beta + conv_epsilon ) ){
std::cout << "*********************************" << std::endl;
std::cout << "Convergence ! at iteration: " << itr+1 << std::endl;
std::cout << "*********************************" << std::endl;
if(!end_of_cooc || need_to_cooc == true) break;
need_to_cooc = true;
}
lpx_add_rows(lp,1); // Add one row constraint s.t. sum( uyh - beta ) <= 0
for (unsigned int i = 0; i < gnum; ++i){
index[i+1] = COL(i);
value[i+1] = result[i] * corlab[i];
}
index[gnum+1] = COL(gnum);
value[gnum+1] = -1;
lpx_set_mat_row(lp, ROW(itr+1), gnum+1, index, value);
lpx_set_row_bnds(lp, ROW(itr+1), LPX_UP, 0.0, 0.0);
model.weight.push_back(0);
model.dfs_vector.push_back(dfscode);
lpx_simplex(lp);
beta = lpx_get_obj_val(lp);
for (unsigned int i = 0; i < gnum; ++i){
double new_weight;
new_weight = lpx_get_col_prim(lp, COL(i));
if(new_weight < 0) new_weight = 0; // weight > 0
weight[i] = new_weight;
}
margin = lpx_get_row_dual(lp, ROW(0));
double margin_error = 0.0;
for (unsigned int i = 0; i < gnum; ++i) { // summarizing hypotheses
if (corlab[i]*result[i] < margin){
++margin_error;
}
}
margin_error /= gnum;
//next rule is estimated
wbias = 0.0;
for (unsigned int i = 0; i < gnum; ++i){
wbias += corlab[i] * weight[i];
}
std::ofstream os (out);
if (! os) {
std::cerr << "FATAL: Cannot open output file: " << out << std::endl;
return;
}
os.setf(std::ios::fixed,std::ios::floatfield);
os.precision(12);
for (unsigned int r = 0; r < itr; ++r){
model.weight[r] = - lpx_get_row_dual(lp, ROW(r+1));
if(model.weight[r] < 0) model.weight[r] = 0; // alpha > 0
os << model.flag[r] * model.weight[r] << "\t" << model.dfs_vector[r] << std::endl;
std::cout << model.flag[r] * model.weight[r] << "\t" << model.dfs_vector[r] << std::endl;
}
std::cout << "After iteration " << itr+1 << std::endl;
std::cout << "Margin: " << margin << std::endl;
std::cout << "Margin Error: " << margin_error << std::endl;
}
std::cout << "end lpboost" << std::endl;
}
