Source code for cvxpy.atoms.affine.conv

"""
Copyright 2013 Steven Diamond

This file is part of CVXPY.

CVXPY is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

CVXPY is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with CVXPY.  If not, see <http://www.gnu.org/licenses/>.
"""

from cvxpy.atoms.affine.affine_atom import AffAtom
import cvxpy.utilities as u
import cvxpy.interface as intf
import cvxpy.lin_ops.lin_utils as lu
import numpy as np


[docs]class conv(AffAtom): """ 1D discrete convolution of two vectors. The discrete convolution :math:`c` of vectors :math:`a` and :math:`b` of lengths :math:`n` and :math:`m`, respectively, is a length-:math:`(n+m-1)` vector where .. math:: c_k = \\sum_{i+j=k} a_ib_j, \quad k=0, \ldots, n+m-2. Parameters ---------- lh_expr : Constant A constant 1D vector or a 2D column vector. rh_expr : Expression A 1D vector or a 2D column vector. """ # TODO work with right hand constant. def __init__(self, lh_expr, rh_expr): super(conv, self).__init__(lh_expr, rh_expr) @AffAtom.numpy_numeric def numeric(self, values): """Convolve the two values. """ # Convert values to 1D. values = list(map(intf.from_2D_to_1D, values)) return np.convolve(values[0], values[1]) def validate_arguments(self): """Checks that both arguments are vectors, and the first is constant. """ if not self.args[0].is_vector() or not self.args[1].is_vector(): raise ValueError("The arguments to conv must resolve to vectors.") if not self.args[0].is_constant(): raise ValueError("The first argument to conv must be constant.") def shape_from_args(self): """The sum of the argument dimensions - 1. """ lh_length = self.args[0].shape[0] rh_length = self.args[1].shape[0] return (lh_length + rh_length - 1, 1) def sign_from_args(self): """Same as times. """ return u.sign.mul_sign(self.args[0], self.args[1]) def is_incr(self, idx): """Is the composition non-decreasing in argument idx? """ return self.args[0].is_nonneg() def is_decr(self, idx): """Is the composition non-increasing in argument idx? """ return self.args[0].is_nonpos() @staticmethod def graph_implementation(arg_objs, shape, data=None): """Convolve two vectors. Parameters ---------- arg_objs : list LinExpr for each argument. shape : tuple The shape of the resulting expression. data : Additional data required by the atom. Returns ------- tuple (LinOp for objective, list of constraints) """ return (lu.conv(arg_objs[0], arg_objs[1], shape), [])