Source code for cvxpy.atoms.log_sum_exp

Copyright 2013 Steven Diamond

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
See the License for the specific language governing permissions and
limitations under the License.

from cvxpy.atoms.atom import Atom
from cvxpy.atoms.axis_atom import AxisAtom
import numpy as np
from scipy.special import logsumexp

[docs]class log_sum_exp(AxisAtom): """:math:`\\log\\sum_i e^{x_i}` """ def __init__(self, x, axis=None, keepdims=False): super(log_sum_exp, self).__init__(x, axis=axis, keepdims=keepdims) @Atom.numpy_numeric def numeric(self, values): """Evaluates e^x elementwise, sums, and takes the log. """ return logsumexp(values[0], axis=self.axis, keepdims=self.keepdims) def _grad(self, values): """Gives the (sub/super)gradient of the atom w.r.t. each argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: values: A list of numeric values for the arguments. Returns: A list of SciPy CSC sparse matrices or None. """ return self._axis_grad(values) def _column_grad(self, value): """Gives the (sub/super)gradient of the atom w.r.t. a column argument. Matrix expressions are vectorized, so the gradient is a matrix. Args: value: A numeric value for a column. Returns: A NumPy ndarray or None. """ denom = np.exp(logsumexp(value, axis=None, keepdims=True)) nom = np.exp(value) D = nom/denom return D def sign_from_args(self): """Returns sign (is positive, is negative) of the expression. """ return (False, False) def is_atom_convex(self): """Is the atom convex? """ return True def is_atom_concave(self): """Is the atom concave? """ return False def is_incr(self, idx): """Is the composition non-decreasing in argument idx? """ return True def is_decr(self, idx): """Is the composition non-increasing in argument idx? """ return False