def matvec_plus_vec_times_scalar(mat1, vec1, vec2, scalar):
# TODO: fix docstring
"""
:param mat1: a numpy ndarray of shape (m, n).
:param vec1: a coniclifts Variable of shape (n,) or (n, 1)
:param vec2: a numpy ndarray of shape (m,) or (m, 1).
:param scalar: a coniclifts Variable of size 1.
:return:
Return A_rows, A_cols, A_vals as if they were generated by a compile() call on
con = (mat @ vecvar + vec * singlevar >= 0).
"""
if isinstance(scalar, Expression):
if scalar.size == 1:
scalar = scalar.item()
else:
raise ValueError('Argument `scalar` must have size 1.')
# We can now assume that "scalar" is a ScalarExpression.
if isinstance(vec1, Variable):
b = scalar.offset * vec2
a2c = scalar.atoms_to_coeffs.items()
s_covec = np.array([co for (sv, co) in a2c])
s_indices = [sv.id for (sv, co) in a2c]
mat2 = np.outer(vec2, s_covec) # rank 1
mat = np.hstack((mat1, mat2))
indices = vec1.scalar_variable_ids + s_indices
A_vals, A_rows, A_cols = _matvec_by_var_indices(mat, indices)
else:
mat = np.hstack([mat1, np.reshape(vec2, (-1, 1))])
if isinstance(scalar, ScalarExpression):
scalar = Expression([scalar])
expr = concatenate((vec1, scalar))
A_vals, A_rows, A_cols, b = matvec(mat, expr)
return A_vals, A_rows, A_cols, b
def test_concatenate(self):
a = np.array([[1, 2], [3, 4]])
a_cl = Variable(shape=a.shape)
a_cl.value = a
b = np.array([[5, 6]])
b_cl = Variable(shape=b.shape)
b_cl.value = b
expected = np.concatenate((a, b))
actual = aff.concatenate((a_cl, b_cl))
assert np.allclose(expected, actual.value)
expected1 = np.concatenate((b, a))
actual1 = aff.concatenate((b_cl, a_cl))
assert np.allclose(expected1, actual1.value)
a_cl.value = 1 + a
assert not np.allclose(expected, actual.value)
assert not np.allclose(expected1, actual1.value)
def matvec_plus_matvec(mat1, vec1, mat2, vec2):
# TODO: fix docstring
"""
:param mat1: a numpy ndarray of shape (m, n1).
:param vec1: a coniclifts Variable of shape (n1,) or (n1, 1).
:param mat2: a numpy ndarray of shape (m, n2)
:param vec2: a coniclifts Variable of shape (n2,) or (n2, 1).
:return: A_vals, A_rows, A_cols so that the coniclifts Expression
expr = mat1 @ vecvar1 + mat2 @ vecvar2 would have "expr >= 0"
compile to A_vals, A_rows, A_cols, np.zeros((m,)), [].
"""
mat = np.hstack([mat1, mat2])
if isinstance(vec1, Variable) and isinstance(vec2, Variable):
num_rows = mat.shape[0]
b = np.zeros(num_rows)
ids = vec1.scalar_variable_ids + vec2.scalar_variable_ids
A_vals, A_rows, A_cols = _matvec_by_var_indices(mat, ids)
else:
vec = concatenate((vec1, vec2))
A_vals, A_rows, A_cols, b = matvec(mat, vec)
return A_vals, A_rows, A_cols, b