def quad_form(self, expr):
"""Extract quadratic, linear constant parts of a quadratic objective.
"""
# Insert no-op such that root is never a quadratic form, for easier
# processing
root = LinOp(NO_OP, expr.shape, [expr], [])
# Replace quadratic forms with dummy variables.
quad_forms = replace_quad_forms(root, {})
# Calculate affine parts and combine them with quadratic forms to get
# the coefficients.
coeffs, constant = self.extract_quadratic_coeffs(root.args[0],
quad_forms)
# Restore expression.
restore_quad_forms(root.args[0], quad_forms)
# Sort variables corresponding to their starting indices, in ascending
# order.
offsets = sorted(list(self.id_map.items()), key=operator.itemgetter(1))
# Concatenate quadratic matrices and vectors
P = sp.csr_matrix((0, 0))
q = np.zeros(0)
for var_id, offset in offsets:
if var_id in coeffs:
P = sp.block_diag([P, coeffs[var_id]['P']])
q = np.concatenate([q, coeffs[var_id]['q']])
else:
shape = self.var_shapes[var_id]
size = np.prod(shape, dtype=int)
P = sp.block_diag([P, sp.csr_matrix((size, size))])
q = np.concatenate([q, np.zeros(size)])
# TODO(akshayka): This chain of != smells of a bug.
if P.shape[0] != P.shape[1] != self.N or q.shape[0] != self.N:
raise RuntimeError("Resulting quadratic form does not have "
"appropriate dimensions")
if constant.size != 1:
raise RuntimeError("Constant must be a scalar")
return P.tocsr(), q, constant[0]
def quad_form(self, expr):
"""Extract quadratic, linear constant parts of a quadratic objective.
"""
# Insert no-op such that root is never a quadratic form, for easier
# processing
root = LinOp(NO_OP, expr.shape, [expr], [])
# Replace quadratic forms with dummy variables.
quad_forms = replace_quad_forms(root, {})
# Calculate affine parts and combine them with quadratic forms to get
# the coefficients.
coeffs, constant = self.extract_quadratic_coeffs(
root.args[0], quad_forms)
# Restore expression.
restore_quad_forms(root.args[0], quad_forms)
# Sort variables corresponding to their starting indices, in ascending
# order.
offsets = sorted(self.id_map.items(), key=operator.itemgetter(1))
# Extract quadratic matrices and vectors
num_params = constant.shape[1]
P_list = []
q_list = []
P_height = 0
P_entries = 0
for var_id, offset in offsets:
shape = self.var_shapes[var_id]
size = np.prod(shape, dtype=int)
if var_id in coeffs and 'P' in coeffs[var_id]:
P = coeffs[var_id]['P']
P_entries += P[0].size
else:
P = ([], ([], []), (size, size))
if var_id in coeffs and 'q' in coeffs[var_id]:
q = coeffs[var_id]['q']
else:
q = np.zeros((size, num_params))
P_list.append(P)
q_list.append(q)
P_height += size
if P_height != self.x_length:
raise RuntimeError("Resulting quadratic form does not have "
"appropriate dimensions")
# Conceptually we build a block diagonal matrix
# out of all the Ps, then flatten the first two dimensions.
# eg P1
# P2
# We do this by extending each P with zero blocks above and below.
gap_above = np.int64(0)
acc_height = np.int64(0)
rows = np.zeros(P_entries)
cols = np.zeros(P_entries)
vals = np.zeros(P_entries)
entry_offset = 0
for P in P_list:
"""Conceptually, the code is equivalent to
```
above = np.zeros((gap_above, P.shape[1], num_params))
below = np.zeros((gap_below, P.shape[1], num_params))
padded_P = np.concatenate([above, P, below], axis=0)
padded_P = np.reshape(padded_P, (P_height*P.shape[1], num_params),
order='F')
padded_P_list.append(padded_P)
```
but done by constructing a COO matrix.
"""
P_vals, (P_rows, P_cols), P_shape = P
if len(P_vals) > 0:
vals[entry_offset:entry_offset +
P_vals.size] = P_vals.flatten(order='F')
base_rows = gap_above + acc_height + P_rows + P_cols * P_height
full_rows = np.tile(base_rows, num_params)
rows[entry_offset:entry_offset + P_vals.size] = full_rows
full_cols = np.repeat(np.arange(num_params), P_cols.size)
cols[entry_offset:entry_offset + P_vals.size] = full_cols
entry_offset += P_vals.size
gap_above += P_shape[0]
acc_height += P_height * np.int64(P_shape[1])
# Stitch together Ps and qs and constant.
P = sp.coo_matrix((vals, (rows, cols)), shape=(acc_height, num_params))
# Stack q with constant offset as last row.
q = np.vstack(q_list)
q = np.vstack([q, constant.A])
q = sp.csr_matrix(q)
return P, q
def quad_form(self, expr):
"""Extract quadratic, linear constant parts of a quadratic objective.
"""
# Insert no-op such that root is never a quadratic form, for easier
# processing
root = LinOp(NO_OP, expr.shape, [expr], [])
# Replace quadratic forms with dummy variables.
quad_forms = replace_quad_forms(root, {})
# Calculate affine parts and combine them with quadratic forms to get
# the coefficients.
coeff_list, constant = self.extract_quadratic_coeffs(
root.args[0], quad_forms)
# Restore expression.
restore_quad_forms(root.args[0], quad_forms)
# Sort variables corresponding to their starting indices, in ascending
# order.
offsets = sorted(self.id_map.items(), key=operator.itemgetter(1))
# Get P and q for each parameter.
P_list = []
q_list = []
for coeffs in coeff_list:
# Concatenate quadratic matrices and vectors
P = sp.csr_matrix((0, 0))
q = np.zeros(0)
for var_id, offset in offsets:
if var_id in coeffs:
P = sp.block_diag([P, coeffs[var_id]['P']])
q = np.concatenate([q, coeffs[var_id]['q']])
else:
shape = self.var_shapes[var_id]
size = np.prod(shape, dtype=int)
P = sp.block_diag([P, sp.csr_matrix((size, size))])
q = np.concatenate([q, np.zeros(size)])
if (P.shape[0] != P.shape[1] and P.shape[1] != self.x_length) or \
q.shape[0] != self.x_length:
raise RuntimeError("Resulting quadratic form does not have "
"appropriate dimensions")
if not np.isscalar(constant) and constant.size > 1:
raise RuntimeError("Constant must be a scalar")
P_size = P.shape[0] * P.shape[1]
P_list.append(P.reshape((P_size, 1), order='F'))
q_list.append(q)
# Here we assemble the Ps and qs into matrices
# that we multiply by a parameter vector to get P, q
# i.e. [P1.flatten(), P2.flatten(), ...]
# [q1, q2, ...]
# where Pi, qi are coefficients for the ith entry of
# the parameter vector.
# Stitch together Ps and qs and constant.
P = sp.hstack(P_list)
# Stack q with constant offset as last row.
q = np.stack(q_list, axis=1)
q = sp.vstack([q, constant])
return P, q
