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 not 'A' in probdata or not 'b' in probdata or not 'c' 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 kwargs.pop('use_indirect', False): # False by default
import _scs_indirect
return _scs_indirect.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)
def solve(probdata, cone, opts={}, USE_INDIRECT=False):
""" This Python routine "unpacks" scipy sparse matrix A into the
data structures that we need for calling scs routine.
It is *not* compatible with CVXOPT spmatrix and matrix, although
it would not be very difficult to make it compatible. We put the
onus on the user to convert CVXOPT matrix types into numpy, scipy
array types.
"""
if not 'A' in probdata or not 'b' in probdata or not 'c' in probdata:
raise TypeError("Missing 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 USE_INDIRECT:
return _scs_indirect.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone, opts, warm)
else:
return _scs_direct.csolve((m, n), Adata, Aindices, Acolptr, b, c, cone, opts, warm)
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)