def get_problem_matrix(constrs, id_to_col=None, constr_offsets=None):
"""
Builds a sparse representation of the problem data by calling CVXCanon's
C++ build_matrix function.
Parameters
----------
constrs: A list of python linOp trees
id_to_col: A map from variable id to offset withoun our matrix
Returns
----------
V, I, J: numpy arrays encoding a sparse representation of our problem
const_vec: a numpy column vector representing the constant_data in our problem
"""
linOps = [constr.expr for constr in constrs]
lin_vec = cvxcore.LinOpVector()
id_to_col_C = cvxcore.IntIntMap()
if id_to_col is None:
id_to_col = {}
# Loading the variable offsets from our
# Python map into a C++ map
for id, col in list(id_to_col.items()):
id_to_col_C[int(id)] = int(col)
# This array keeps variables data in scope
# after build_lin_op_tree returns
tmp = []
for lin in linOps:
tree = build_lin_op_tree(lin, tmp)
tmp.append(tree)
lin_vec.push_back(tree)
if constr_offsets is None:
problemData = cvxcore.build_matrix(lin_vec, id_to_col_C)
else:
# Load constraint offsets into a C++ vector
constr_offsets_C = cvxcore.IntVector()
for offset in constr_offsets:
constr_offsets_C.push_back(int(offset))
problemData = cvxcore.build_matrix(lin_vec, id_to_col_C,
constr_offsets_C)
# Unpacking
V = problemData.getV(len(problemData.V))
I = problemData.getI(len(problemData.I))
J = problemData.getJ(len(problemData.J))
const_vec = problemData.getConstVec(len(problemData.const_vec))
return V, I, J, const_vec.reshape(-1, 1)
def get_problem_matrix(linOps, var_length, id_to_col, param_to_size,
param_to_col, constr_length):
"""
Builds a sparse representation of the problem data.
Parameters
----------
linOps: A list of python linOp trees representing an affine expression
var_length: The total length of the variables.
id_to_col: A map from variable id to column offset.
param_to_size: A map from parameter id to parameter size.
param_to_col: A map from parameter id to column in tensor.
constr_length: Summed sizes of constraints input.
Returns
-------
A sparse (CSC) matrix with constr_length * (var_length + 1) rows and
param_size + 1 columns (where param_size is the length of the
parameter vector).
"""
lin_vec = cvxcore.ConstLinOpVector()
id_to_col_C = cvxcore.IntIntMap()
for id, col in id_to_col.items():
id_to_col_C[int(id)] = int(col)
param_to_size_C = cvxcore.IntIntMap()
for id, size in param_to_size.items():
param_to_size_C[int(id)] = int(size)
# dict to memoize construction of C++ linOps, and to keep Python references
# to them to prevent their deletion
linPy_to_linC = {}
for lin in linOps:
build_lin_op_tree(lin, linPy_to_linC)
tree = linPy_to_linC[lin]
lin_vec.push_back(tree)
problemData = cvxcore.build_matrix(lin_vec, int(var_length), id_to_col_C,
param_to_size_C, s.get_num_threads())
# Populate tensors with info from problemData.
tensor_V = {}
tensor_I = {}
tensor_J = {}
for param_id, size in param_to_size.items():
tensor_V[param_id] = []
tensor_I[param_id] = []
tensor_J[param_id] = []
problemData.param_id = param_id
for i in range(size):
problemData.vec_idx = i
prob_len = problemData.getLen()
tensor_V[param_id].append(problemData.getV(prob_len))
tensor_I[param_id].append(problemData.getI(prob_len))
tensor_J[param_id].append(problemData.getJ(prob_len))
# Reduce tensors to a single sparse CSR matrix.
V = []
I = []
J = []
# one of the 'parameters' in param_to_col is a constant scalar offset,
# hence 'plus_one'
param_size_plus_one = 0
for param_id, col in param_to_col.items():
size = param_to_size[param_id]
param_size_plus_one += size
for i in range(size):
V.append(tensor_V[param_id][i])
I.append(tensor_I[param_id][i] +
tensor_J[param_id][i] * constr_length)
J.append(tensor_J[param_id][i] * 0 + (i + col))
V = np.concatenate(V)
I = np.concatenate(I)
J = np.concatenate(J)
A = scipy.sparse.csc_matrix(
(V, (I, J)),
shape=(np.int64(constr_length) * np.int64(var_length + 1),
param_size_plus_one))
return A