static PyObject* Bar_getkind(BarObject *self, void *closure) {
PyObject *retval=NULL;
int kind = GLP_CV;
if (!Bar_Valid(self, 1)) return NULL;
if (!Bar_Row(self)) {
kind = glp_get_col_kind(LP, Bar_Index(self)+1);
}
switch (kind) {
case GLP_CV: retval = (PyObject*)&PyFloat_Type; break;
case GLP_IV: retval = (PyObject*)&PyInt_Type; break;
case GLP_BV: retval = (PyObject*)&PyBool_Type; break;
default:
PyErr_SetString(PyExc_RuntimeError,
"unexpected variable kind encountered");
return NULL;
}
Py_INCREF(retval);
return retval;
}
static void
solve(char* file_name) {
ppl_Constraint_System_t ppl_cs;
#ifndef NDEBUG
ppl_Constraint_System_t ppl_cs_copy;
#endif
ppl_Generator_t optimum_location;
ppl_Linear_Expression_t ppl_le;
int dimension, row, num_rows, column, nz, i, j, type;
int* coefficient_index;
double lb, ub;
double* coefficient_value;
mpq_t rational_lb, rational_ub;
mpq_t* rational_coefficient;
mpq_t* objective;
ppl_Linear_Expression_t ppl_objective_le;
ppl_Coefficient_t optimum_n;
ppl_Coefficient_t optimum_d;
mpq_t optimum;
mpz_t den_lcm;
int optimum_found;
glp_mpscp glpk_mpscp;
glpk_lp = glp_create_prob();
glp_init_mpscp(&glpk_mpscp);
if (verbosity == 0) {
/* FIXME: find a way to suppress output from glp_read_mps. */
}
#ifdef PPL_LPSOL_SUPPORTS_TIMINGS
if (print_timings)
start_clock();
#endif /* defined(PPL_LPSOL_SUPPORTS_TIMINGS) */
if (glp_read_mps(glpk_lp, GLP_MPS_FILE, &glpk_mpscp, file_name) != 0)
fatal("cannot read MPS file `%s'", file_name);
#ifdef PPL_LPSOL_SUPPORTS_TIMINGS
if (print_timings) {
fprintf(stderr, "Time to read the input file: ");
print_clock(stderr);
fprintf(stderr, " s\n");
start_clock();
}
#endif /* defined(PPL_LPSOL_SUPPORTS_TIMINGS) */
glpk_lp_num_int = glp_get_num_int(glpk_lp);
if (glpk_lp_num_int > 0 && !no_mip && !use_simplex)
fatal("the enumeration solving method can not handle MIP problems");
dimension = glp_get_num_cols(glpk_lp);
/* Read variables constrained to be integer. */
if (glpk_lp_num_int > 0 && !no_mip && use_simplex) {
if (verbosity >= 4)
fprintf(output_file, "Integer variables:\n");
integer_variables = (ppl_dimension_type*)
malloc((glpk_lp_num_int + 1)*sizeof(ppl_dimension_type));
for (i = 0, j = 0; i < dimension; ++i) {
int col_kind = glp_get_col_kind(glpk_lp, i+1);
if (col_kind == GLP_IV || col_kind == GLP_BV) {
integer_variables[j] = i;
if (verbosity >= 4) {
ppl_io_fprint_variable(output_file, i);
fprintf(output_file, " ");
}
++j;
}
}
}
coefficient_index = (int*) malloc((dimension+1)*sizeof(int));
coefficient_value = (double*) malloc((dimension+1)*sizeof(double));
rational_coefficient = (mpq_t*) malloc((dimension+1)*sizeof(mpq_t));
ppl_new_Constraint_System(&ppl_cs);
mpq_init(rational_lb);
mpq_init(rational_ub);
for (i = 1; i <= dimension; ++i)
mpq_init(rational_coefficient[i]);
mpz_init(den_lcm);
if (verbosity >= 4)
fprintf(output_file, "\nConstraints:\n");
/* Set up the row (ordinary) constraints. */
num_rows = glp_get_num_rows(glpk_lp);
for (row = 1; row <= num_rows; ++row) {
/* Initialize the least common multiple computation. */
mpz_set_si(den_lcm, 1);
/* Set `nz' to the number of non-zero coefficients. */
nz = glp_get_mat_row(glpk_lp, row, coefficient_index, coefficient_value);
for (i = 1; i <= nz; ++i) {
set_mpq_t_from_double(rational_coefficient[i], coefficient_value[i]);
/* Update den_lcm. */
mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_coefficient[i]));
}
lb = glp_get_row_lb(glpk_lp, row);
ub = glp_get_row_ub(glpk_lp, row);
set_mpq_t_from_double(rational_lb, lb);
set_mpq_t_from_double(rational_ub, ub);
mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_lb));
mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_ub));
ppl_new_Linear_Expression_with_dimension(&ppl_le, dimension);
for (i = 1; i <= nz; ++i) {
mpz_mul(tmp_z, den_lcm, mpq_numref(rational_coefficient[i]));
mpz_divexact(tmp_z, tmp_z, mpq_denref(rational_coefficient[i]));
ppl_assign_Coefficient_from_mpz_t(ppl_coeff, tmp_z);
ppl_Linear_Expression_add_to_coefficient(ppl_le, coefficient_index[i]-1,
ppl_coeff);
}
type = glp_get_row_type(glpk_lp, row);
add_constraints(ppl_le, type, rational_lb, rational_ub, den_lcm, ppl_cs);
ppl_delete_Linear_Expression(ppl_le);
}
free(coefficient_value);
for (i = 1; i <= dimension; ++i)
mpq_clear(rational_coefficient[i]);
free(rational_coefficient);
free(coefficient_index);
#ifndef NDEBUG
ppl_new_Constraint_System_from_Constraint_System(&ppl_cs_copy, ppl_cs);
#endif
/*
FIXME: here we could build the polyhedron and minimize it before
adding the variable bounds.
*/
/* Set up the columns constraints, i.e., variable bounds. */
for (column = 1; column <= dimension; ++column) {
lb = glp_get_col_lb(glpk_lp, column);
ub = glp_get_col_ub(glpk_lp, column);
set_mpq_t_from_double(rational_lb, lb);
set_mpq_t_from_double(rational_ub, ub);
/* Initialize the least common multiple computation. */
mpz_set_si(den_lcm, 1);
mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_lb));
mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_ub));
ppl_new_Linear_Expression_with_dimension(&ppl_le, dimension);
ppl_assign_Coefficient_from_mpz_t(ppl_coeff, den_lcm);
ppl_Linear_Expression_add_to_coefficient(ppl_le, column-1, ppl_coeff);
type = glp_get_col_type(glpk_lp, column);
add_constraints(ppl_le, type, rational_lb, rational_ub, den_lcm, ppl_cs);
ppl_delete_Linear_Expression(ppl_le);
}
mpq_clear(rational_ub);
mpq_clear(rational_lb);
/* Deal with the objective function. */
objective = (mpq_t*) malloc((dimension+1)*sizeof(mpq_t));
/* Initialize the least common multiple computation. */
mpz_set_si(den_lcm, 1);
mpq_init(objective[0]);
set_mpq_t_from_double(objective[0], glp_get_obj_coef(glpk_lp, 0));
for (i = 1; i <= dimension; ++i) {
mpq_init(objective[i]);
set_mpq_t_from_double(objective[i], glp_get_obj_coef(glpk_lp, i));
/* Update den_lcm. */
mpz_lcm(den_lcm, den_lcm, mpq_denref(objective[i]));
}
/* Set the ppl_objective_le to be the objective function. */
ppl_new_Linear_Expression_with_dimension(&ppl_objective_le, dimension);
/* Set value for objective function's inhomogeneous term. */
mpz_mul(tmp_z, den_lcm, mpq_numref(objective[0]));
mpz_divexact(tmp_z, tmp_z, mpq_denref(objective[0]));
ppl_assign_Coefficient_from_mpz_t(ppl_coeff, tmp_z);
ppl_Linear_Expression_add_to_inhomogeneous(ppl_objective_le, ppl_coeff);
/* Set values for objective function's variable coefficients. */
for (i = 1; i <= dimension; ++i) {
mpz_mul(tmp_z, den_lcm, mpq_numref(objective[i]));
mpz_divexact(tmp_z, tmp_z, mpq_denref(objective[i]));
ppl_assign_Coefficient_from_mpz_t(ppl_coeff, tmp_z);
ppl_Linear_Expression_add_to_coefficient(ppl_objective_le, i-1, ppl_coeff);
}
if (verbosity >= 4) {
fprintf(output_file, "Objective function:\n");
if (mpz_cmp_si(den_lcm, 1) != 0)
fprintf(output_file, "(");
ppl_io_fprint_Linear_Expression(output_file, ppl_objective_le);
}
for (i = 0; i <= dimension; ++i)
mpq_clear(objective[i]);
free(objective);
if (verbosity >= 4) {
if (mpz_cmp_si(den_lcm, 1) != 0) {
fprintf(output_file, ")/");
mpz_out_str(output_file, 10, den_lcm);
}
fprintf(output_file, "\n%s\n",
(maximize ? "Maximizing." : "Minimizing."));
}
ppl_new_Coefficient(&optimum_n);
ppl_new_Coefficient(&optimum_d);
ppl_new_Generator_zero_dim_point(&optimum_location);
optimum_found = use_simplex
? solve_with_simplex(ppl_cs,
ppl_objective_le,
optimum_n,
optimum_d,
optimum_location)
: solve_with_generators(ppl_cs,
ppl_objective_le,
optimum_n,
optimum_d,
optimum_location);
ppl_delete_Linear_Expression(ppl_objective_le);
if (glpk_lp_num_int > 0)
free(integer_variables);
if (optimum_found) {
mpq_init(optimum);
ppl_Coefficient_to_mpz_t(optimum_n, tmp_z);
mpq_set_num(optimum, tmp_z);
ppl_Coefficient_to_mpz_t(optimum_d, tmp_z);
mpz_mul(tmp_z, tmp_z, den_lcm);
mpq_set_den(optimum, tmp_z);
if (verbosity == 1)
fprintf(output_file, "Optimized problem.\n");
if (verbosity >= 2)
fprintf(output_file, "Optimum value: %.10g\n", mpq_get_d(optimum));
if (verbosity >= 3) {
fprintf(output_file, "Optimum location:\n");
ppl_Generator_divisor(optimum_location, ppl_coeff);
ppl_Coefficient_to_mpz_t(ppl_coeff, tmp_z);
for (i = 0; i < dimension; ++i) {
mpz_set(mpq_denref(tmp1_q), tmp_z);
ppl_Generator_coefficient(optimum_location, i, ppl_coeff);
ppl_Coefficient_to_mpz_t(ppl_coeff, mpq_numref(tmp1_q));
ppl_io_fprint_variable(output_file, i);
fprintf(output_file, " = %.10g\n", mpq_get_d(tmp1_q));
}
}
#ifndef NDEBUG
{
ppl_Polyhedron_t ph;
unsigned int relation;
ppl_new_C_Polyhedron_recycle_Constraint_System(&ph, ppl_cs_copy);
ppl_delete_Constraint_System(ppl_cs_copy);
relation = ppl_Polyhedron_relation_with_Generator(ph, optimum_location);
ppl_delete_Polyhedron(ph);
assert(relation == PPL_POLY_GEN_RELATION_SUBSUMES);
}
#endif
maybe_check_results(PPL_MIP_PROBLEM_STATUS_OPTIMIZED,
mpq_get_d(optimum));
mpq_clear(optimum);
}
ppl_delete_Constraint_System(ppl_cs);
ppl_delete_Coefficient(optimum_d);
ppl_delete_Coefficient(optimum_n);
ppl_delete_Generator(optimum_location);
glp_delete_prob(glpk_lp);
}
int lpx_get_col_kind(LPX *lp, int j)
{ /* retrieve column kind */
return glp_get_col_kind(lp, j) == GLP_CV ? LPX_CV : LPX_IV;
}
static int branch_drtom(glp_tree *T, int *_next)
{ glp_prob *mip = T->mip;
int m = mip->m;
int n = mip->n;
char *non_int = T->non_int;
int j, jj, k, t, next, kase, len, stat, *ind;
double x, dk, alfa, delta_j, delta_k, delta_z, dz_dn, dz_up,
dd_dn, dd_up, degrad, *val;
/* basic solution of LP relaxation must be optimal */
xassert(glp_get_status(mip) == GLP_OPT);
/* allocate working arrays */
ind = xcalloc(1+n, sizeof(int));
val = xcalloc(1+n, sizeof(double));
/* nothing has been chosen so far */
jj = 0, degrad = -1.0;
/* walk through the list of columns (structural variables) */
for (j = 1; j <= n; j++)
{ /* if j-th column is not marked as fractional, skip it */
if (!non_int[j]) continue;
/* obtain (fractional) value of j-th column in basic solution
of LP relaxation */
x = glp_get_col_prim(mip, j);
/* since the value of j-th column is fractional, the column is
basic; compute corresponding row of the simplex table */
len = glp_eval_tab_row(mip, m+j, ind, val);
/* the following fragment computes a change in the objective
function: delta Z = new Z - old Z, where old Z is the
objective value in the current optimal basis, and new Z is
the objective value in the adjacent basis, for two cases:
1) if new upper bound ub' = floor(x[j]) is introduced for
j-th column (down branch);
2) if new lower bound lb' = ceil(x[j]) is introduced for
j-th column (up branch);
since in both cases the solution remaining dual feasible
becomes primal infeasible, one implicit simplex iteration
is performed to determine the change delta Z;
it is obvious that new Z, which is never better than old Z,
is a lower (minimization) or upper (maximization) bound of
the objective function for down- and up-branches. */
for (kase = -1; kase <= +1; kase += 2)
{ /* if kase < 0, the new upper bound of x[j] is introduced;
in this case x[j] should decrease in order to leave the
basis and go to its new upper bound */
/* if kase > 0, the new lower bound of x[j] is introduced;
in this case x[j] should increase in order to leave the
basis and go to its new lower bound */
/* apply the dual ratio test in order to determine which
auxiliary or structural variable should enter the basis
to keep dual feasibility */
k = glp_dual_rtest(mip, len, ind, val, kase, 1e-9);
if (k != 0) k = ind[k];
/* if no non-basic variable has been chosen, LP relaxation
of corresponding branch being primal infeasible and dual
unbounded has no primal feasible solution; in this case
the change delta Z is formally set to infinity */
if (k == 0)
{ delta_z =
(T->mip->dir == GLP_MIN ? +DBL_MAX : -DBL_MAX);
goto skip;
}
/* row of the simplex table that corresponds to non-basic
variable x[k] choosen by the dual ratio test is:
x[j] = ... + alfa * x[k] + ...
where alfa is the influence coefficient (an element of
the simplex table row) */
/* determine the coefficient alfa */
for (t = 1; t <= len; t++) if (ind[t] == k) break;
xassert(1 <= t && t <= len);
alfa = val[t];
/* since in the adjacent basis the variable x[j] becomes
non-basic, knowing its value in the current basis we can
determine its change delta x[j] = new x[j] - old x[j] */
delta_j = (kase < 0 ? floor(x) : ceil(x)) - x;
/* and knowing the coefficient alfa we can determine the
corresponding change delta x[k] = new x[k] - old x[k],
where old x[k] is a value of x[k] in the current basis,
and new x[k] is a value of x[k] in the adjacent basis */
delta_k = delta_j / alfa;
/* Tomlin noticed that if the variable x[k] is of integer
kind, its change cannot be less (eventually) than one in
the magnitude */
if (k > m && glp_get_col_kind(mip, k-m) != GLP_CV)
{ /* x[k] is structural integer variable */
if (fabs(delta_k - floor(delta_k + 0.5)) > 1e-3)
{ if (delta_k > 0.0)
delta_k = ceil(delta_k); /* +3.14 -> +4 */
else
delta_k = floor(delta_k); /* -3.14 -> -4 */
}
}
/* now determine the status and reduced cost of x[k] in the
current basis */
if (k <= m)
{ stat = glp_get_row_stat(mip, k);
dk = glp_get_row_dual(mip, k);
}
else
{ stat = glp_get_col_stat(mip, k-m);
dk = glp_get_col_dual(mip, k-m);
}
/* if the current basis is dual degenerate, some reduced
costs which are close to zero may have wrong sign due to
round-off errors, so correct the sign of d[k] */
switch (T->mip->dir)
{ case GLP_MIN:
if (stat == GLP_NL && dk < 0.0 ||
stat == GLP_NU && dk > 0.0 ||
stat == GLP_NF) dk = 0.0;
break;
case GLP_MAX:
if (stat == GLP_NL && dk > 0.0 ||
stat == GLP_NU && dk < 0.0 ||
stat == GLP_NF) dk = 0.0;
break;
default:
xassert(T != T);
}
/* now knowing the change of x[k] and its reduced cost d[k]
we can compute the corresponding change in the objective
function delta Z = new Z - old Z = d[k] * delta x[k];
note that due to Tomlin's modification new Z can be even
worse than in the adjacent basis */
delta_z = dk * delta_k;
skip: /* new Z is never better than old Z, therefore the change
delta Z is always non-negative (in case of minimization)
or non-positive (in case of maximization) */
switch (T->mip->dir)
{ case GLP_MIN: xassert(delta_z >= 0.0); break;
case GLP_MAX: xassert(delta_z <= 0.0); break;
default: xassert(T != T);
}
/* save the change in the objective fnction for down- and
up-branches, respectively */
if (kase < 0) dz_dn = delta_z; else dz_up = delta_z;
}
/* thus, in down-branch no integer feasible solution can be
better than Z + dz_dn, and in up-branch no integer feasible
solution can be better than Z + dz_up, where Z is value of
the objective function in the current basis */
/* following the heuristic by Driebeck and Tomlin we choose a
column (i.e. structural variable) which provides largest
degradation of the objective function in some of branches;
besides, we select the branch with smaller degradation to
be solved next and keep other branch with larger degradation
in the active list hoping to minimize the number of further
backtrackings */
if (degrad < fabs(dz_dn) || degrad < fabs(dz_up))
{ jj = j;
if (fabs(dz_dn) < fabs(dz_up))
{ /* select down branch to be solved next */
next = GLP_DN_BRNCH;
degrad = fabs(dz_up);
}
else
{ /* select up branch to be solved next */
next = GLP_UP_BRNCH;
degrad = fabs(dz_dn);
}
/* save the objective changes for printing */
dd_dn = dz_dn, dd_up = dz_up;
/* if down- or up-branch has no feasible solution, we does
not need to consider other candidates (in principle, the
corresponding branch could be pruned right now) */
if (degrad == DBL_MAX) break;
}
}
/* free working arrays */
xfree(ind);
xfree(val);
/* something must be chosen */
xassert(1 <= jj && jj <= n);
#if 1 /* 02/XI-2009 */
if (degrad < 1e-6 * (1.0 + 0.001 * fabs(mip->obj_val)))
{ jj = branch_mostf(T, &next);
goto done;
}
#endif
if (T->parm->msg_lev >= GLP_MSG_DBG)
{ xprintf("branch_drtom: column %d chosen to branch on\n", jj);
if (fabs(dd_dn) == DBL_MAX)
xprintf("branch_drtom: down-branch is infeasible\n");
else
xprintf("branch_drtom: down-branch bound is %.9e\n",
lpx_get_obj_val(mip) + dd_dn);
if (fabs(dd_up) == DBL_MAX)
xprintf("branch_drtom: up-branch is infeasible\n");
else
xprintf("branch_drtom: up-branch bound is %.9e\n",
lpx_get_obj_val(mip) + dd_up);
}
done: *_next = next;
return jj;
}
int c_glp_get_col_kind (glp_prob *lp, int i){
return glp_get_col_kind (lp, i);
}
// retrieve all missing values of LP/MILP
void Rglpk_retrieve_MP_from_file (char **file, int *type,
int *lp_n_constraints,
int *lp_n_objective_vars,
double *lp_objective_coefficients,
int *lp_constraint_matrix_i,
int *lp_constraint_matrix_j,
double *lp_constraint_matrix_values,
int *lp_direction_of_constraints,
double *lp_right_hand_side,
double *lp_left_hand_side,
int *lp_objective_var_is_integer,
int *lp_objective_var_is_binary,
int *lp_bounds_type,
double *lp_bounds_lower,
double *lp_bounds_upper,
int *lp_ignore_first_row,
int *lp_verbosity,
char **lp_constraint_names,
char **lp_objective_vars_names
) {
extern glp_prob *lp;
glp_tran *tran;
const char *str;
int i, j, lp_column_kind, tmp;
int ind_offset, status;
// Turn on/off Terminal Output
if (*lp_verbosity==1)
glp_term_out(GLP_ON);
else
glp_term_out(GLP_OFF);
// create problem object
if (lp)
glp_delete_prob(lp);
lp = glp_create_prob();
// read file -> gets stored as an GLPK problem object 'lp'
// which file type do we have?
switch (*type){
case 1:
// Fixed (ancient) MPS Format, param argument currently NULL
status = glp_read_mps(lp, GLP_MPS_DECK, NULL, *file);
break;
case 2:
// Free (modern) MPS format, param argument currently NULL
status = glp_read_mps(lp, GLP_MPS_FILE, NULL, *file);
break;
case 3:
// CPLEX LP Format
status = glp_read_lp(lp, NULL, *file);
break;
case 4:
// MATHPROG Format (based on lpx_read_model function)
tran = glp_mpl_alloc_wksp();
status = glp_mpl_read_model(tran, *file, 0);
if (!status) {
status = glp_mpl_generate(tran, NULL);
if (!status) {
glp_mpl_build_prob(tran, lp);
}
}
glp_mpl_free_wksp(tran);
break;
}
// if file read successfully glp_read_* returns zero
if ( status != 0 ) {
glp_delete_prob(lp);
lp = NULL;
error("Reading file %c failed.", *file);
}
if(*lp_verbosity==1)
Rprintf("Retrieve column specific data ...\n");
if(glp_get_num_cols(lp) != *lp_n_objective_vars) {
glp_delete_prob(lp);
lp = NULL;
error("The number of columns is not as specified");
}
// retrieve column specific data (values, bounds and type)
for (i = 0; i < *lp_n_objective_vars; i++) {
lp_objective_coefficients[i] = glp_get_obj_coef(lp, i+1);
// Note that str must not be freed befor we have returned
// from the .C call in R!
str = glp_get_col_name(lp, i+1);
if (str){
lp_objective_vars_names[i] = (char *) str;
}
lp_bounds_type[i] = glp_get_col_type(lp, i+1);
lp_bounds_lower[i] = glp_get_col_lb (lp, i+1);
lp_bounds_upper[i] = glp_get_col_ub (lp, i+1);
lp_column_kind = glp_get_col_kind(lp, i+1);
// set to TRUE if objective variable is integer or binary
switch (lp_column_kind){
case GLP_IV:
lp_objective_var_is_integer[i] = 1;
break;
case GLP_BV:
lp_objective_var_is_binary[i] = 1;
break;
}
}
ind_offset = 0;
if(*lp_verbosity==1)
Rprintf("Retrieve row specific data ...\n");
if(glp_get_num_rows(lp) != *lp_n_constraints) {
glp_delete_prob(lp);
lp = NULL;
error("The number of rows is not as specified");
}
// retrieve row specific data (right hand side, direction of constraints)
for (i = *lp_ignore_first_row; i < *lp_n_constraints; i++) {
lp_direction_of_constraints[i] = glp_get_row_type(lp, i+1);
str = glp_get_row_name(lp, i + 1);
if (str) {
lp_constraint_names[i] = (char *) str;
}
// the right hand side. Note we don't allow for double bounded or
// free auxiliary variables
if( lp_direction_of_constraints[i] == GLP_LO )
lp_right_hand_side[i] = glp_get_row_lb(lp, i+1);
if( lp_direction_of_constraints[i] == GLP_UP )
lp_right_hand_side[i] = glp_get_row_ub(lp, i+1);
if( lp_direction_of_constraints[i] == GLP_FX )
lp_right_hand_side[i] = glp_get_row_lb(lp, i+1);
if( lp_direction_of_constraints[i] == GLP_DB ){
lp_right_hand_side[i] = glp_get_row_ub(lp, i+1);
lp_left_hand_side[i] = glp_get_row_lb(lp, i+1);
}
tmp = glp_get_mat_row(lp, i+1, &lp_constraint_matrix_j[ind_offset-1],
&lp_constraint_matrix_values[ind_offset-1]);
if (tmp > 0)
for (j = 0; j < tmp; j++)
lp_constraint_matrix_i[ind_offset+j] = i+1;
ind_offset += tmp;
}
if(*lp_verbosity==1)
Rprintf("Done.\n");
}
