static PyObject *phase1_sdp
(PyObject *self, PyObject *args, PyObject *kwrds) {
matrix *u;
spmatrix *Ai,*Ao;
int_t k,i,j,n,m,nnz,nz,col;
if(!PyArg_ParseTuple(args,"OO",&Ai,&u)) return NULL;
n = (int_t) sqrt((double)SP_NROWS(Ai));
m = SP_NCOLS(Ai) - 1;
nnz = SP_NNZ(Ai) - SP_COL(Ai)[1] + 1 + m + n + 1;
Ao = SpMatrix_New((n+2)*(n+2),m+2,nnz,DOUBLE);
if (!Ao) return PyErr_NoMemory();
// A_0
SP_VALD(Ao)[0] = 1.0;
SP_ROW(Ao)[0] = n*(n+2)+n;
SP_COL(Ao)[0] = 0;
SP_COL(Ao)[1] = 1;
// A_i, i=1,..,m
for (i=1;i<=m;i++){
k = SP_COL(Ao)[i];
nz = SP_COL(Ai)[i+1]-SP_COL(Ai)[i]; // nonzeros in Ai
// copy Ai
memcpy(SP_VALD(Ao)+k,SP_VALD(Ai)+SP_COL(Ai)[i],nz*sizeof(double));
// insert -u[i]
SP_VALD(Ao)[k+nz] = -MAT_BUFD(u)[i-1];
// update row colptr
SP_COL(Ao)[i+1] = SP_COL(Ao)[i]+nz+1;
// generate row indices
for (j=0;j<nz;j++) {
col = SP_ROW(Ai)[SP_COL(Ai)[i]+j] / n;
SP_ROW(Ao)[k+j] = SP_ROW(Ai)[SP_COL(Ai)[i]+j] + col*2;
}
SP_ROW(Ao)[k+nz] = n*(n+2)+n;
}
// last constraint
k = SP_COL(Ao)[m+1];
for (i=0;i<n;i++){
SP_VALD(Ao)[k+i] = 1.0;
SP_ROW(Ao)[k+i] = i*(n+2)+i;
}
SP_VALD(Ao)[k+n] = 1.0;
SP_ROW(Ao)[k+n] = (n+2)*(n+2)-1;
SP_COL(Ao)[m+2] = SP_COL(Ao)[m+1] + n + 1;
return (PyObject*) Ao;
}
static cholmod_sparse * create_matrix(spmatrix *A)
{
cholmod_sparse *B;
if (!(B = CHOL(allocate_sparse)(SP_NROWS(A), SP_NCOLS(A), 0,
1, 0, 0, (SP_ID(A) == DOUBLE ? CHOLMOD_REAL : CHOLMOD_COMPLEX),
&Common))) return NULL;
int i;
for (i=0; i<SP_NCOLS(A); i++)
((int_t *)B->nz)[i] = SP_COL(A)[i+1] - SP_COL(A)[i];
B->x = SP_VAL(A);
B->i = SP_ROW(A);
B->nzmax = SP_NNZ(A);
memcpy(B->p, SP_COL(A), (SP_NCOLS(A)+1)*sizeof(int_t));
return B;
}
static PyObject* splinsolve(PyObject *self, PyObject *args,
PyObject *kwrds)
{
spmatrix *A, *B, *X;
matrix *P=NULL;
int n, nnz;
cholmod_sparse *Ac=NULL, *Bc=NULL, *Xc=NULL;
cholmod_factor *L=NULL;
#if PY_MAJOR_VERSION >= 3
int uplo_='L';
#endif
char uplo='L';
char *kwlist[] = {"A", "B", "p", "uplo", NULL};
if (!set_options()) return NULL;
#if PY_MAJOR_VERSION >= 3
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OO|OC", kwlist, &A,
&B, &P, &uplo_)) return NULL;
uplo = (char) uplo_;
#else
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OO|Oc", kwlist, &A,
&B, &P, &uplo)) return NULL;
#endif
if (!SpMatrix_Check(A) || SP_NROWS(A) != SP_NCOLS(A))
PY_ERR_TYPE("A is not a square sparse matrix");
n = SP_NROWS(A);
nnz = SP_NNZ(A);
if (!SpMatrix_Check(B) || SP_ID(A) != SP_ID(B))
PY_ERR_TYPE("B must be a sparse matrix of the same type as A");
if (SP_NROWS(B) != n)
PY_ERR(PyExc_ValueError, "incompatible dimensions for B");
if (P) {
if (!Matrix_Check(P) || MAT_ID(P) != INT) err_int_mtrx("p");
if (MAT_LGT(P) != n) err_buf_len("p");
if (!CHOL(check_perm)(P->buffer, n, n, &Common))
PY_ERR(PyExc_ValueError, "not a valid permutation");
}
if (uplo != 'U' && uplo != 'L') err_char("uplo", "'L', 'U'");
if (!(Ac = pack(A, uplo))) return PyErr_NoMemory();
L = CHOL(analyze_p) (Ac, P ? MAT_BUFI(P): NULL, NULL, 0, &Common);
if (Common.status != CHOLMOD_OK){
CHOL(free_factor)(&L, &Common);
CHOL(free_sparse)(&Ac, &Common);
if (Common.status == CHOLMOD_OUT_OF_MEMORY)
return PyErr_NoMemory();
else {
PyErr_SetString(PyExc_ValueError, "symbolic factorization "
"failed");
return NULL;
}
}
CHOL(factorize) (Ac, L, &Common);
CHOL(free_sparse)(&Ac, &Common);
if (Common.status > 0) switch (Common.status) {
case CHOLMOD_NOT_POSDEF:
PyErr_SetObject(PyExc_ArithmeticError, Py_BuildValue("i",
L->minor));
CHOL(free_factor)(&L, &Common);
return NULL;
break;
case CHOLMOD_DSMALL:
/* This never happens unless we change the default value
* of Common.dbound (0.0). */
if (L->is_ll)
PyErr_Warn(PyExc_RuntimeWarning, "tiny diagonal "
"elements in L");
else
PyErr_Warn(PyExc_RuntimeWarning, "tiny diagonal "
"elements in D");
break;
default:
PyErr_Warn(PyExc_UserWarning, "");
}
if (L->minor<n) {
CHOL(free_factor)(&L, &Common);
PY_ERR(PyExc_ArithmeticError, "singular matrix");
}
if (!(Bc = create_matrix(B))) {
CHOL(free_factor)(&L, &Common);
return PyErr_NoMemory();
}
Xc = CHOL(spsolve)(0, L, Bc, &Common);
free_matrix(Bc);
CHOL(free_factor)(&L, &Common);
if (Common.status != CHOLMOD_OK){
CHOL(free_sparse)(&Xc, &Common);
if (Common.status == CHOLMOD_OUT_OF_MEMORY)
return PyErr_NoMemory();
else
PY_ERR(PyExc_ValueError, "solve step failed");
}
if (!(X = SpMatrix_New(Xc->nrow, Xc->ncol,
((int_t*)Xc->p)[Xc->ncol], SP_ID(A)))) {
CHOL(free_sparse)(&Xc, &Common);
return PyErr_NoMemory();
}
memcpy(SP_COL(X), (int_t *) Xc->p, (Xc->ncol+1)*sizeof(int_t));
memcpy(SP_ROW(X), (int_t *) Xc->i,
((int_t *) Xc->p)[Xc->ncol]*sizeof(int_t));
memcpy(SP_VAL(X), (double *) Xc->x,
((int_t *) Xc->p)[Xc->ncol]*E_SIZE[SP_ID(X)]);
CHOL(free_sparse)(&Xc, &Common);
return (PyObject *) X;
}
static PyObject* linsolve(PyObject *self, PyObject *args,
PyObject *kwrds)
{
spmatrix *A;
matrix *B, *P=NULL;
int i, n, nnz, oB=0, ldB=0, nrhs=-1;
cholmod_sparse *Ac=NULL;
cholmod_factor *L=NULL;
cholmod_dense *x=NULL, *b=NULL;
void *b_old;
#if PY_MAJOR_VERSION >= 3
int uplo_ = 'L';
#endif
char uplo='L';
char *kwlist[] = {"A", "B", "p", "uplo", "nrhs", "ldB", "offsetB",
NULL};
if (!set_options()) return NULL;
#if PY_MAJOR_VERSION >= 3
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OO|OCiii", kwlist,
&A, &B, &P, &uplo_, &nrhs, &ldB, &oB)) return NULL;
uplo = (char) uplo_;
#else
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OO|Ociii", kwlist,
&A, &B, &P, &uplo, &nrhs, &ldB, &oB)) return NULL;
#endif
if (!SpMatrix_Check(A) || SP_NROWS(A) != SP_NCOLS(A))
PY_ERR_TYPE("A is not a sparse matrix");
n = SP_NROWS(A);
nnz = SP_NNZ(A);
if (!Matrix_Check(B) || MAT_ID(B) != SP_ID(A))
PY_ERR_TYPE("B must be a dense matrix of the same numerical "
"type as A");
if (nrhs < 0) nrhs = MAT_NCOLS(B);
if (n == 0 || nrhs == 0) return Py_BuildValue("");
if (ldB == 0) ldB = MAX(1,MAT_NROWS(B));
if (ldB < MAX(1,n)) err_ld("ldB");
if (oB < 0) err_nn_int("offsetB");
if (oB + (nrhs-1)*ldB + n > MAT_LGT(B)) err_buf_len("B");
if (P) {
if (!Matrix_Check(P) || MAT_ID(P) != INT) err_int_mtrx("p");
if (MAT_LGT(P) != n) err_buf_len("p");
if (!CHOL(check_perm)(P->buffer, n, n, &Common))
PY_ERR(PyExc_ValueError, "not a valid permutation");
}
if (uplo != 'U' && uplo != 'L') err_char("uplo", "'L', 'U'");
if (!(Ac = pack(A, uplo))) return PyErr_NoMemory();
L = CHOL(analyze_p)(Ac, P ? MAT_BUFI(P): NULL, NULL, 0, &Common);
if (Common.status != CHOLMOD_OK){
free_matrix(Ac);
CHOL(free_sparse)(&Ac, &Common);
CHOL(free_factor)(&L, &Common);
if (Common.status == CHOLMOD_OUT_OF_MEMORY)
return PyErr_NoMemory();
else {
PyErr_SetString(PyExc_ValueError, "symbolic factorization "
"failed");
return NULL;
}
}
CHOL(factorize) (Ac, L, &Common);
CHOL(free_sparse)(&Ac, &Common);
if (Common.status < 0) {
CHOL(free_factor)(&L, &Common);
switch (Common.status) {
case CHOLMOD_OUT_OF_MEMORY:
return PyErr_NoMemory();
default:
PyErr_SetString(PyExc_ValueError, "factorization "
"failed");
return NULL;
}
}
if (Common.status > 0) switch (Common.status) {
case CHOLMOD_NOT_POSDEF:
PyErr_SetObject(PyExc_ArithmeticError,
Py_BuildValue("i", L->minor));
CHOL(free_factor)(&L, &Common);
return NULL;
break;
case CHOLMOD_DSMALL:
/* This never happens unless we change the default value
* of Common.dbound (0.0). */
if (L->is_ll)
PyErr_Warn(PyExc_RuntimeWarning, "tiny diagonal "
"elements in L");
else
PyErr_Warn(PyExc_RuntimeWarning, "tiny diagonal "
"elements in D");
break;
default:
PyErr_Warn(PyExc_UserWarning, "");
}
if (L->minor<n) {
CHOL(free_factor)(&L, &Common);
PY_ERR(PyExc_ArithmeticError, "singular matrix");
}
b = CHOL(allocate_dense)(n, 1, n, (MAT_ID(B) == DOUBLE ?
CHOLMOD_REAL : CHOLMOD_COMPLEX) , &Common);
if (Common.status == CHOLMOD_OUT_OF_MEMORY) {
CHOL(free_factor)(&L, &Common);
CHOL(free_dense)(&b, &Common);
return PyErr_NoMemory();
}
b_old = b->x;
for (i=0; i<nrhs; i++) {
b->x = MAT_BUF(B) + (i*ldB + oB)*E_SIZE[MAT_ID(B)];
x = CHOL(solve) (CHOLMOD_A, L, b, &Common);
if (Common.status != CHOLMOD_OK){
PyErr_SetString(PyExc_ValueError, "solve step failed");
CHOL(free_factor)(&L, &Common);
b->x = b_old;
CHOL(free_dense)(&b, &Common);
CHOL(free_dense)(&x, &Common);
return NULL;
}
memcpy(b->x, x->x, SP_NROWS(A)*E_SIZE[MAT_ID(B)]);
CHOL(free_dense)(&x, &Common);
}
b->x = b_old;
CHOL(free_dense)(&b, &Common);
CHOL(free_factor)(&L, &Common);
return Py_BuildValue("");
}
static PyObject *robustLS_to_sdp
(PyObject *self, PyObject *args, PyObject *kwrds) {
PyObject *Alist,*bt, *Ai;
spmatrix *A,*b;
int_t m,n,mp,np,pt,i,j,k,N,nnz=0,ri=0;
char *kwlist[] = {"Alist","bt",NULL};
if(!PyArg_ParseTupleAndKeywords(args,kwrds,"OO",kwlist,&Alist,&bt)) return NULL;
if(!PyList_Check(Alist)) {
PyErr_SetString(PyExc_TypeError,"Alist must be a list of matrices");
return NULL;
}
// get pt = p + 1
pt = PyList_Size(Alist);
// get size of bt
if(Matrix_Check(bt)){
m = MAT_NROWS(bt);
np = MAT_NCOLS(bt);
}
else if (SpMatrix_Check(bt)){
m = SP_NROWS(bt);
np = SP_NCOLS(bt);
}
else {
PyErr_SetString(PyExc_TypeError,"b must be a vector");
return NULL;
}
if (np!=1) {
PyErr_SetString(PyExc_TypeError,"b must be a vector");
return NULL;
}
// get n and check A0
if (!(Ai = PyList_GetItem(Alist,0))) return NULL;
if (Matrix_Check(Ai)) {
n = MAT_NCOLS(Ai);
nnz += m*n;
}
else if (SpMatrix_Check(Ai)) {
n = SP_NCOLS(Ai);
nnz += SP_NNZ(Ai);
}
else {
PyErr_SetString(PyExc_TypeError,"only spmatrix and matrix types allowed");
return NULL;
}
// check remaining matrices in Alist
for (i=1;i<pt;i++) {
if (!(Ai = PyList_GetItem(Alist,i))) return NULL;
if (Matrix_Check(Ai)) {
mp = MAT_NROWS(Ai);
np = MAT_NCOLS(Ai);
nnz += m*n;
}
else if (SpMatrix_Check(Ai)) {
mp = SP_NROWS(Ai);
np = SP_NCOLS(Ai);
nnz += SP_NNZ(Ai);
}
else {
PyErr_SetString(PyExc_TypeError,"only spmatrix and matrix types allowed");
return NULL;
}
if (!(mp==m && np==n)){
PyErr_SetString(PyExc_TypeError,"matrices in Alist must have same size");
return NULL;
}
}
nnz += 2*m + pt;
// generate b
b = SpMatrix_New(n+2,1,2,DOUBLE);
if (!b) return PyErr_NoMemory();
SP_COL(b)[0] = 0;
SP_VALD(b)[0] = -1;
SP_ROW(b)[0] = 0;
SP_VALD(b)[1] = -1;
SP_ROW(b)[1] = 1;
SP_COL(b)[1] = 2;
// generate A
N = m+pt;
A = SpMatrix_New(N*N,n+3,nnz,DOUBLE);
if (!A) return PyErr_NoMemory();
// build A0
SP_COL(A)[0] = ri;
for(i=0;i<m;i++){
if(SpMatrix_Check(bt)){
SP_VALD(A)[ri] = -SP_VALD(bt)[i];
SP_ROW(A)[ri++] = pt+i;
}
else{
SP_VALD(A)[ri] = -MAT_BUFD(bt)[i];
SP_ROW(A)[ri++] = pt+i;
}
}
for(i=0;i<m;i++) {
SP_VALD(A)[ri] = 1;
SP_ROW(A)[ri++] = (N+1)*pt + i*N+i;
}
// build A1
SP_COL(A)[1] = ri;
for(i=0;i<pt-1;i++){
SP_VALD(A)[ri] = -1;
SP_ROW(A)[ri++] = N+1 + i*N+i;
}
// build A2
SP_COL(A)[2] = ri;
SP_VALD(A)[ri] = -1;
SP_ROW(A)[ri++] = 0;
SP_COL(A)[3] = ri;
// build A3,...
for(j=0;j<n;j++){
// generate col. i
for(i=0;i<pt;i++){
Ai = PyList_GetItem(Alist,i);
if(SpMatrix_Check(Ai)) {
nnz = SP_COL(Ai)[j+1]-SP_COL(Ai)[j];
for(k=0;k<nnz;k++) {
SP_VALD(A)[ri] = -SP_VALD(Ai)[SP_COL(Ai)[j]+k];
SP_ROW(A)[ri++] = pt+i*N + SP_ROW(Ai)[SP_COL(Ai)[j]+k];
}
}
else {
for (k=0;k<m;k++) {
SP_VALD(A)[ri] = -MAT_BUFD(Ai)[j*m+k];
SP_ROW(A)[ri++] = pt+i*N + k;
}
}
}
SP_COL(A)[j+4] = ri;
}
return Py_BuildValue("NN",A,b);
}
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;
}