Source code for cvxpy.atoms.quad_over_lin

"""
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.atom import Atom
import cvxpy.lin_ops.lin_utils as lu
from cvxpy.constraints.second_order import SOC
import numpy as np
import scipy.sparse as sp
import scipy as scipy


[docs]class quad_over_lin(Atom): """ :math:`(sum_{ij}X^2_{ij})/y` """ def __init__(self, x, y): super(quad_over_lin, self).__init__(x, y) @Atom.numpy_numeric def numeric(self, values): """Returns the sum of the entries of x squared over y. """ return np.square(values[0]).sum()/values[1] def _domain(self): """Returns constraints describing the domain of the node. """ # y > 0. return [self.args[1] >= 0] 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. """ X = values[0] y = values[1] if y <= 0: return [None, None] else: # DX = 2X/y, Dy = -||X||^2_2/y^2 Dy = -np.square(X).sum()/np.square(y) Dy = sp.csc_matrix(Dy) DX = 2.0*X/y DX = np.reshape(DX, (self.args[0].size, 1)) DX = scipy.sparse.csc_matrix(DX) return [DX, Dy] def shape_from_args(self): """Returns the (row, col) shape of the expression. """ return tuple() def sign_from_args(self): """Returns sign (is positive, is negative) of the expression. """ # Always positive. return (True, 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 (idx == 0) and self.args[idx].is_nonneg() def is_decr(self, idx): """Is the composition non-increasing in argument idx? """ return ((idx == 0) and self.args[idx].is_nonpos()) or (idx == 1) def validate_arguments(self): """Check dimensions of arguments. """ if not self.args[1].is_scalar(): raise ValueError("The second argument to quad_over_lin must be a scalar.") super(quad_over_lin, self).validate_arguments() def is_quadratic(self): """Quadratic if x is affine and y is constant. """ return self.args[0].is_affine() and self.args[1].is_constant() def is_qpwa(self): """Quadratic of piecewise affine if x is PWL and y is constant. """ return self.args[0].is_pwl() and self.args[1].is_constant() @staticmethod def graph_implementation(arg_objs, shape, data=None): """Reduces the atom to an affine expression and list of constraints. 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) """ x = arg_objs[0] y = arg_objs[1] # Known to be a scalar. v = lu.create_var((1, 1)) two = lu.create_const(2, (1, 1)) t = lu.sum_expr([y, v]) X_shape = (y.shape[0] + x.shape[0], 1) X = lu.vstack([lu.sub_expr(y, v), lu.mul_expr(two, x, x.shape)], X_shape) constraints = [SOC(t, X), lu.create_geq(y)] return (v, constraints)