static PyObject *
normal(PyObject *self, PyObject *args, PyObject *kwrds)
{
matrix *obj;
int i, nrows, ncols = 1;
double m = 0, s = 1;
char *kwlist[] = {"nrows", "ncols", "mean", "std", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "i|idd", kwlist,
&nrows, &ncols, &m, &s)) return NULL;
if (s < 0.0) PY_ERR(PyExc_ValueError, "std must be non-negative");
if ((nrows<0) || (ncols<0)) {
PyErr_SetString(PyExc_TypeError, "dimensions must be non-negative");
return NULL;
}
if (!(obj = Matrix_New(nrows, ncols, DOUBLE)))
return PyErr_NoMemory();
gsl_rng_env_setup();
rng_type = gsl_rng_default;
rng = gsl_rng_alloc (rng_type);
gsl_rng_set(rng, seed);
for (i = 0; i < nrows*ncols; i++)
MAT_BUFD(obj)[i] = gsl_ran_gaussian (rng, s) + m;
seed = gsl_rng_get (rng);
gsl_rng_free(rng);
return (PyObject *)obj;
}
static PyObject *
uniform(PyObject *self, PyObject *args, PyObject *kwrds)
{
matrix *obj;
int i, nrows, ncols = 1;
double a = 0, b = 1;
char *kwlist[] = {"nrows", "ncols", "a", "b", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "i|idd", kwlist,
&nrows, &ncols, &a, &b)) return NULL;
if (a>b) PY_ERR(PyExc_ValueError, "a must be less than b");
if ((nrows<0) || (ncols<0))
PY_ERR_TYPE("dimensions must be non-negative");
if (!(obj = (matrix *)Matrix_New(nrows, ncols, DOUBLE)))
return PyErr_NoMemory();
gsl_rng_env_setup();
rng_type = gsl_rng_default;
rng = gsl_rng_alloc (rng_type);
gsl_rng_set(rng, seed);
for (i= 0; i < nrows*ncols; i++)
MAT_BUFD(obj)[i] = gsl_ran_flat (rng, a, b);
seed = gsl_rng_get (rng);
gsl_rng_free(rng);
return (PyObject *)obj;
}
static PyObject* diag(PyObject *self, PyObject *args)
{
PyObject *F;
matrix *d=NULL;
cholmod_factor *L;
#if PY_MAJOR_VERSION >= 3
const char *descr;
#else
char *descr;
#endif
int k, strt, incx=1, incy, nrows, ncols;
if (!set_options()) return NULL;
if (!PyArg_ParseTuple(args, "O", &F)) return NULL;
#if PY_MAJOR_VERSION >= 3
if (!PyCapsule_CheckExact(F) || !(descr = PyCapsule_GetName(F)))
err_CO("F");
if (strncmp(descr, "CHOLMOD FACTOR", 14))
PY_ERR_TYPE("F is not a CHOLMOD factor");
L = (cholmod_factor *) PyCapsule_GetPointer(F, descr);
#else
if (!PyCObject_Check(F)) err_CO("F");
descr = PyCObject_GetDesc(F);
if (!descr || strncmp(descr, "CHOLMOD FACTOR", 14))
PY_ERR_TYPE("F is not a CHOLMOD factor");
L = (cholmod_factor *) PyCObject_AsVoidPtr(F);
#endif
/* Check factorization */
if (L->xtype == CHOLMOD_PATTERN || L->minor<L->n || !L->is_ll
|| !L->is_super)
PY_ERR(PyExc_ValueError, "F must be a nonsingular supernodal "
"Cholesky factor");
if (!(d = Matrix_New(L->n,1,L->xtype == CHOLMOD_REAL ? DOUBLE :
COMPLEX))) return PyErr_NoMemory();
strt = 0;
for (k=0; k<L->nsuper; k++){
/* x[L->px[k], .... ,L->px[k+1]-1] is a dense lower-triangular
* nrowx times ncols matrix. We copy its diagonal to
* d[strt, ..., strt+ncols-1] */
ncols = (int)((int_t *) L->super)[k+1] -
((int_t *) L->super)[k];
nrows = (int)((int_t *) L->pi)[k+1] - ((int_t *) L->pi)[k];
incy = nrows+1;
if (MAT_ID(d) == DOUBLE)
dcopy_(&ncols, ((double *) L->x) + ((int_t *) L->px)[k],
&incy, MAT_BUFD(d)+strt, &incx);
else
zcopy_(&ncols, ((double complex *) L->x) + ((int_t *) L->px)[k],
&incy, MAT_BUFZ(d)+strt, &incx);
strt += ncols;
}
return (PyObject *)d;
}
static PyObject *ind2sub
(PyObject *self, PyObject *args)
{
matrix *Im;
int_t i;
int_t n;
if (!PyArg_ParseTuple(args, "nO", &n, &Im)) return NULL;
matrix *Il = Matrix_New(MAT_NROWS(Im),1,INT);
if (!Il) return PyErr_NoMemory();
matrix *Jl = Matrix_New(MAT_NROWS(Im),1,INT);
if (!Il) return PyErr_NoMemory();
for (i=0;i< MAT_NROWS(Im);i++) {
MAT_BUFI(Il)[i] = MAT_BUFI(Im)[i] % n;
MAT_BUFI(Jl)[i] = MAT_BUFI(Im)[i] / n;
}
return Py_BuildValue("NN", Il, Jl);
}
static PyObject *nzcolumns
(PyObject *self, PyObject *args)
{
PyObject *A;
matrix *Nz;
int_t m,n,i,j,p,nnz,sum, *tmp;
if (!PyArg_ParseTuple(args,"O",&A)) return NULL;
n = (int_t) sqrt((double)SP_NROWS(A));
m = SP_NCOLS(A)-1;
Nz = Matrix_New(m,1,INT);
if (!Nz) return PyErr_NoMemory();
tmp = malloc(n*sizeof(int_t));
//tmp = Matrix_New(n,1,INT);
if (!tmp) return PyErr_NoMemory();
// erase workspace
for (i=0;i<n;i++) tmp[i] = 0;
for (j=0;j<m;j++){
p = SP_COL(A)[j+1];
nnz = SP_COL(A)[j+2]-p;
if (nnz) {
// Find nonzero cols
for (i=0;i<nnz;i++) {
tmp[SP_ROW(A)[p+i] % n] += 1;
tmp[SP_ROW(A)[p+i] / n] += 1;
}
// Count nonzero cols and reset workspace
MAT_BUFI(Nz)[j] = 0;
sum = 0;
#pragma omp parallel for shared(tmp,Nz,j,n) private(i) reduction(+:sum)
for (i=0;i<n;i++) {
if(tmp[i]) {
tmp[i] = 0;
sum++;
}
}
MAT_BUFI(Nz)[j] = sum;
}
}
free(tmp);
return (PyObject*) Nz;
}
static PyObject *sub2ind
(PyObject *self, PyObject *args)
{
matrix *Im,*Jm;
PyObject *siz;
int_t i;
int_t m,n;
if (!PyArg_ParseTuple(args, "OOO", &siz, &Im, &Jm)) return NULL;
if (!PyArg_ParseTuple(siz, "nn", &m, &n)) return NULL;
matrix *Ind = Matrix_New(MAT_NROWS(Im),1,INT);
if (!Ind) return PyErr_NoMemory();
for (i=0;i< MAT_NROWS(Im) ;i++) {
// Add data check:
MAT_BUFI(Ind)[i] = MAT_BUFI(Im)[i] + m*MAT_BUFI(Jm)[i];
}
return Py_BuildValue("N", Ind);
}
static PyObject *toeplitz
(PyObject *self, PyObject *args, PyObject *kwrds) {
PyObject *r=NULL, *c=NULL;
int_t m,n,i,j;
char *kwlist[] = {"c","r",NULL};
if (!PyArg_ParseTupleAndKeywords(args,kwrds,"O|O",kwlist,&c,&r)) return NULL;
if (!Matrix_Check(c))
return NULL;
if (r==NULL)
r = c;
else if (!Matrix_Check(r))
return NULL;
if (MAT_ID(r) != DOUBLE || MAT_ID(c) != DOUBLE)
return NULL;
if (MAT_NCOLS(r) > 1 || MAT_NCOLS(c) > 1)
return NULL;
m = MAT_NROWS(c);
n = MAT_NROWS(r);
// build dense toeplitz matrix, column by column
matrix *T = Matrix_New(m,n,DOUBLE);
if (!T) return PyErr_NoMemory();
for(j=0;j<n;j++) {
for(i=0;i<(m>j?j:m);i++) {
MAT_BUFD(T)[j*m+i] = MAT_BUFD(r)[j-i];
}
for(i=j;i<m;i++) {
MAT_BUFD(T)[j*m+i] = MAT_BUFD(c)[i-j];
}
}
return (PyObject*) T;
}
static PyObject *matperm
(PyObject *self, PyObject *args)
{
PyObject *nzc;
matrix *pm;
int_t Ns,Nd,m,i,Nmax;
if (!PyArg_ParseTuple(args,"On",&nzc,&Nmax)) return NULL;
m = MAT_NROWS(nzc);
pm = Matrix_New(m,1,INT);
if (!pm) return PyErr_NoMemory();
// Check Nmax
if (Nmax<0) Nmax = 0;
Ns = 0; Nd = 0;
for (i=0;i<m;i++){
if(MAT_BUFI(nzc)[i] > Nmax)
MAT_BUFI(pm)[Nd++] = i;
else
MAT_BUFI(pm)[m-1-Ns++] = i;
}
return Py_BuildValue("Nn",pm,Ns);
}
static PyObject* sdpa_readhead
(PyObject *self, PyObject *args)
{
int i,j,t;
int_t m=0,n=0,nblocks=0;
matrix *bstruct = NULL;
PyObject *f;
char buf[2048]; // buffer
char *info;
if (!PyArg_ParseTuple(args,"O",&f)) return NULL;
#if PY_MAJOR_VERSION >= 3
if (PyUnicode_Check(f)) {
const char* fname = PyUnicode_AsUTF8AndSize(f,NULL);
#else
if (PyString_Check(f)) {
const char* fname = PyString_AsString(f);
#endif
FILE *fp = fopen(fname,"r");
if (!fp) {
return NULL;
}
/* Skip comments and read m */
while (1) {
info = fgets(buf,1024,fp);
if (buf[0] != '*' && buf[0] != '"') {
sscanf(buf,"%d",&i);
break;
}
}
m = (int_t) i;
/* read nblocks */
j = fscanf(fp,"%d",&i);
nblocks = (int_t) i;
/* read blockstruct and compute block offsets*/
bstruct = Matrix_New(nblocks,1,INT);
if (!bstruct) return PyErr_NoMemory();
n = 0;
for (i=0; i<nblocks; i++) {
j = fscanf(fp,"%*[^0-9+-]%d",&t);
MAT_BUFI(bstruct)[i] = (int_t) t;
n += (int_t) labs(MAT_BUFI(bstruct)[i]);
}
fclose(fp);
}
return Py_BuildValue("iiN",n,m,bstruct);
}
static char doc_sdpa_read[] =
"Reads sparse SDPA data file (dat-s).\n"
"\n"
"A,b,bstruct = sdpa_read(f[,neg=False])\n"
"\n"
"PURPOSE\n"
"Reads problem data from sparse SDPA data file for\n"
"the semidefinite programs:\n"
"\n"
" (P) minimize <A0,X>\n"
" subject to <Ai,X> = bi, i = 1,...,m\n"
" X >= 0\n"
"\n"
" (D) maximize b'*y\n"
" subject to sum_i Ai*yi + S = A0\n"
" S >= 0\n"
"\n"
"Here '>=' means that X and S must be positive semidefinite.\n"
"The matrices A0,A1,...Am are symmetric and of order n.\n"
"If the optional argument 'neg' is True, the negative of the\n"
"problem data is returned.\n"
"\n"
"ARGUMENTS\n"
"f Python file object\n"
"\n"
"neg Python boolean (optional)\n"
"\n"
"RETURNS\n"
"A CVXOPT sparse matrix of doubles with columns Ai[:]\n"
" (Only lower trianglular elements of Ai are stored.)\n"
"\n"
"b CVXOPT matrix\n"
"\n"
"bstruct CVXOPT integer matrix\n";
static PyObject* sdpa_read
(PyObject *self, PyObject *args, PyObject *kwrds)
{
int i,j,mno,bno,ii,jj,t;
int_t k,m,n,nblocks,nlines;
double v;
long fpos;
PyObject *f;
PyObject *neg = Py_False;
char *info;
const char* fname;
int_t* boff; // block offset
char buf[2048]; // buffer
char *kwlist[] = {"f","neg",NULL};
if (!PyArg_ParseTupleAndKeywords(args,kwrds,"O|O",kwlist,&f,&neg)) return NULL;
#if PY_MAJOR_VERSION >= 3
if (PyUnicode_Check(f)) fname = PyUnicode_AsUTF8AndSize(f,NULL);
#elif PY_MAJOR_VERSION == 2
if (PyString_Check(f)) fname = PyString_AsString(f);
#endif
FILE *fp = fopen(fname,"r");
if (!fp) {
return NULL;
}
/* Skip comments and read m */
while (1) {
info = fgets(buf,1024,fp);
if (buf[0] != '*' && buf[0] != '"') {
sscanf(buf,"%d",&i);
break;
}
}
m = (int_t) i;
/* read nblocks */
j = fscanf(fp,"%d",&i);
nblocks = (int_t) i;
/* read blockstruct and compute block offsets*/
matrix *bstruct = Matrix_New(nblocks,1,INT);
if (!bstruct) return PyErr_NoMemory();
boff = malloc(sizeof(int_t)*(nblocks+1));
if(!boff) return PyErr_NoMemory();
boff[0] = 0; n = 0;
for (i=0; i<nblocks; i++) {
j = fscanf(fp,"%*[^0-9+-]%d",&t);
MAT_BUFI(bstruct)[i] = (int_t) t;
n += (int_t) labs(MAT_BUFI(bstruct)[i]);
boff[i+1] = n;
}
/* read vector b */
matrix *b = Matrix_New(m,1,DOUBLE);
if (!b) return PyErr_NoMemory();
for (i=0;i<m;i++) {
j = fscanf(fp,"%*[^0-9+-]%lf",&MAT_BUFD(b)[i]);
if (neg == Py_True)
MAT_BUFD(b)[i] *= -1;
}
/* count remaining lines */
fpos = ftell(fp);
for (nlines = 0; fgets(buf, 1023, fp) != NULL; nlines++);
//nlines--;
fseek(fp,fpos,SEEK_SET);
/* Create data matrix A */
spmatrix *A = SpMatrix_New(n*n,m+1,nlines,DOUBLE);
if (!A) return PyErr_NoMemory();
// read data matrices
fseek(fp,fpos,SEEK_SET);
for (i=0,j=-1,k=0;k<nlines;k++){
if (fscanf(fp,"%*[^0-9+-]%d",&mno) <=0 ) break;
if (fscanf(fp,"%*[^0-9+-]%d",&bno) <=0 ) break;
if (fscanf(fp,"%*[^0-9+-]%d",&ii) <=0 ) break;
if (fscanf(fp,"%*[^0-9+-]%d",&jj) <=0 ) break;
if (fscanf(fp,"%*[^0-9+-]%lf",&v) <=0 ) break;
// check that value is nonzero
if (v != 0) {
// add block offset
ii += boff[bno-1];
jj += boff[bno-1];
// insert index and value
SP_ROW(A)[i] = (int_t) ((ii-1)*n + (jj-1));
if (neg == Py_True)
SP_VALD(A)[i] = -v;
else
SP_VALD(A)[i] = v;
// update col. ptr.
while (mno > j)
SP_COL(A)[++j] = i;
i++;
}
}
// update last element(s) of col. ptr.
while (m+1 > j)
SP_COL(A)[++j] = i;
fclose(fp);
// free temp. memory
free(boff);
return Py_BuildValue("NNN",A,b,bstruct);
}
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;
}
static int set_defaults(double *control)
{
int_t pos=0;
int param_id;
PyObject *param, *key, *value;
#if PY_MAJOR_VERSION < 3
char *keystr;
#endif
char err_str[100];
amd_defaults(control);
if (!(param = PyObject_GetAttrString(amd_module, "options")) ||
!PyDict_Check(param)){
PyErr_SetString(PyExc_AttributeError, "missing amd.options"
"dictionary");
return 0;
}
while (PyDict_Next(param, &pos, &key, &value))
#if PY_MAJOR_VERSION >= 3
if ((PyUnicode_Check(key)) &&
get_param_idx(_PyUnicode_AsString(key),¶m_id)) {
if (!PyLong_Check(value) && !PyFloat_Check(value)){
sprintf(err_str, "invalid value for AMD parameter: %-.20s",
_PyUnicode_AsString(key));
#else
if ((keystr = PyString_AsString(key)) && get_param_idx(keystr,
¶m_id)) {
if (!PyInt_Check(value) && !PyFloat_Check(value)){
sprintf(err_str, "invalid value for AMD parameter: "
"%-.20s", keystr);
#endif
PyErr_SetString(PyExc_ValueError, err_str);
Py_DECREF(param);
return 0;
}
control[param_id] = PyFloat_AsDouble(value);
}
Py_DECREF(param);
return 1;
}
static char doc_order[] =
"Computes the approximate minimum degree ordering of a square "
"matrix.\n\n"
"p = order(A, uplo='L')\n\n"
"PURPOSE\n"
"Computes a permutation p that reduces fill-in in the Cholesky\n"
"factorization of A[p,p].\n\n"
"ARGUMENTS\n"
"A square sparse matrix\n\n"
"uplo 'L' or 'U'. If uplo is 'L', the lower triangular part\n"
" of A is used and the upper triangular is ignored. If\n"
" uplo is 'U', the upper triangular part is used and the\n"
" lower triangular part is ignored.\n\n"
"p 'i' matrix of length equal to the order of A";
static PyObject* order_c(PyObject *self, PyObject *args, PyObject *kwrds)
{
spmatrix *A;
matrix *perm;
#if PY_MAJOR_VERSION >= 3
int uplo_ = 'L';
#endif
char uplo = 'L';
int j, k, n, nnz, alloc=0, info;
int_t *rowind=NULL, *colptr=NULL;
double control[AMD_CONTROL];
char *kwlist[] = {"A", "uplo", NULL};
#if PY_MAJOR_VERSION >= 3
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "O|C", kwlist, &A,
&uplo_)) return NULL;
uplo = (char) uplo_;
#else
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "O|c", kwlist, &A,
&uplo)) return NULL;
#endif
if (!set_defaults(control)) return NULL;
if (!SpMatrix_Check(A) || SP_NROWS(A) != SP_NCOLS(A)){
PyErr_SetString(PyExc_TypeError, "A must be a square sparse "
"matrix");
return NULL;
}
if (uplo != 'U' && uplo != 'L') err_char("uplo", "'L', 'U'");
if (!(perm = (matrix *) Matrix_New((int)SP_NROWS(A),1,INT)))
return PyErr_NoMemory();
n = SP_NROWS(A);
for (nnz=0, j=0; j<n; j++) {
if (uplo == 'L'){
for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1] && SP_ROW(A)[k]<j; k++);
nnz += SP_COL(A)[j+1] - k;
}
else {
for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1] && SP_ROW(A)[k] <= j;
k++);
nnz += k - SP_COL(A)[j];
}
}
if (nnz == SP_NNZ(A)){
colptr = (int_t *) SP_COL(A);
rowind = (int_t *) SP_ROW(A);
}
else {
alloc = 1;
colptr = (int_t *) calloc(n+1, sizeof(int_t));
rowind = (int_t *) calloc(nnz, sizeof(int_t));
if (!colptr || !rowind) {
Py_XDECREF(perm); free(colptr); free(rowind);
return PyErr_NoMemory();
}
colptr[0] = 0;
for (j=0; j<n; j++) {
if (uplo == 'L'){
for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1] && SP_ROW(A)[k] < j;
k++);
nnz = SP_COL(A)[j+1] - k;
colptr[j+1] = colptr[j] + nnz;
memcpy(rowind + colptr[j], (int_t *) SP_ROW(A) + k,
nnz*sizeof(int_t));
}
else {
for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1] && SP_ROW(A)[k] <= j;
k++);
nnz = k - SP_COL(A)[j];
colptr[j+1] = colptr[j] + nnz;
memcpy(rowind + colptr[j], (int_t *) (SP_ROW(A) +
SP_COL(A)[j]), nnz*sizeof(int_t));
}
}
}
info = amd_order(n, colptr, rowind, MAT_BUFI(perm), control, NULL);
if (alloc){
free(colptr);
free(rowind);
}
switch (info) {
case AMD_OUT_OF_MEMORY:
Py_XDECREF(perm);
return PyErr_NoMemory();
case AMD_INVALID:
Py_XDECREF(perm);
return Py_BuildValue("");
case AMD_OK:
return (PyObject *) perm;
}
return Py_BuildValue("");
}
static PyMethodDef amd_functions[] = {
{"order", (PyCFunction) order_c, METH_VARARGS|METH_KEYWORDS, doc_order},
{NULL} /* Sentinel */
};
#if PY_MAJOR_VERSION >= 3
static PyModuleDef amd_module_def = {
PyModuleDef_HEAD_INIT,
"amd",
amd__doc__,
-1,
amd_functions,
NULL, NULL, NULL, NULL
};
PyMODINIT_FUNC PyInit_amd(void)
{
if (!(amd_module = PyModule_Create(&amd_module_def))) return NULL;
PyModule_AddObject(amd_module, "options", PyDict_New());
if (import_cvxopt() < 0) return NULL;
return amd_module;
}
#else
PyMODINIT_FUNC initamd(void)
{
amd_module = Py_InitModule3("cvxopt.amd", amd_functions, amd__doc__);
PyModule_AddObject(amd_module, "options", PyDict_New());
if (import_cvxopt() < 0) return;
}
