def solve(probdata, cone, **kwargs):
"""Solves convex cone problems.
@return dictionary with solution with keys:
'x' - primal solution
's' - primal slack solution
'y' - dual solution
'info' - information dictionary
"""
if not probdata or not cone:
raise TypeError('Missing data or cone information')
if 'A' not in probdata or 'b' not in probdata or 'c' not in probdata:
raise TypeError('Missing one or more of A, b, c from data dictionary')
A = probdata['A']
b = probdata['b']
c = probdata['c']
warm = {}
if 'x' in probdata:
warm['x'] = probdata['x']
if 'y' in probdata:
warm['y'] = probdata['y']
if 's' in probdata:
warm['s'] = probdata['s']
if A is None or b is None or c is None:
raise TypeError('Incomplete data specification')
if not sparse.issparse(A):
raise TypeError('A is required to be a sparse matrix')
if not sparse.isspmatrix_csc(A):
warn(
'Converting A to a CSC (compressed sparse column) matrix; may take a '
'while.'
)
A = A.tocsc()
if sparse.issparse(b):
b = b.todense()
if sparse.issparse(c):
c = c.todense()
m, n = A.shape
Adata, Aindices, Acolptr = A.data, A.indices, A.indptr
if kwargs.pop('gpu', False): # False by default
import _scs_gpu
return _scs_gpu.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone, warm,
**kwargs)
if not kwargs.pop('use_indirect', True): # True by default
import _scs_direct
return _scs_direct.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone,
warm, **kwargs)
return _scs_indirect.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone,
warm, **kwargs)
def solve(probdata, cone, **kwargs):
"""Solves convex cone problems.
@return dictionary with solution with keys:
'x' - primal solution
's' - primal slack solution
'y' - dual solution
'info' - information dictionary
"""
if not probdata or not cone:
raise TypeError('Missing data or cone information')
if 'b' not in probdata or 'c' not in probdata:
raise TypeError('Missing one or more of b, c from data dictionary')
b = probdata['b']
c = probdata['c']
m = len(b)
n = len(c)
warm = {}
if 'x' in probdata:
warm['x'] = probdata['x']
if 'y' in probdata:
warm['y'] = probdata['y']
if 's' in probdata:
warm['s'] = probdata['s']
if b is None or c is None:
raise TypeError('Incomplete data specification')
linsys_cbs = kwargs.get('linsys_cbs', None)
if linsys_cbs:
# Create an empty placeholder A matrix that is never used.
A = sparse.csc_matrix((m,n))
else:
if 'A' not in probdata:
raise TypeError('Missing A from data dictionary')
A = probdata['A']
if not sparse.issparse(A):
raise TypeError('A is required to be a sparse matrix')
if not sparse.isspmatrix_csc(A):
warn('Converting A to a CSC (compressed sparse column) matrix; may take a '
'while.')
A = A.tocsc()
if sparse.issparse(b):
b = b.todense()
if sparse.issparse(c):
c = c.todense()
Adata, Aindices, Acolptr = A.data, A.indices, A.indptr
if kwargs.pop('gpu', False): # False by default
if not kwargs.pop('use_indirect', _USE_INDIRECT_DEFAULT):
raise NotImplementedError(
'GPU direct solver not yet available, pass `use_indirect=True`.')
import _scs_gpu
return _scs_gpu.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone, warm,
**kwargs)
if kwargs.pop('use_indirect', _USE_INDIRECT_DEFAULT):
import _scs_indirect
return _scs_indirect.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone,
warm, **kwargs)
if linsys_cbs:
import _scs_python
return _scs_python.csolve(
(m, n), Adata, Aindices, Acolptr, b, c, cone,
warm, **kwargs)
return _scs_direct.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone, warm,
**kwargs)
