"""Convex optimization modeling for cvxopt."""# Copyright (C) 2006-2008 Jacob Mattingley and Stephen Boyd.## This file is part of CVXMOD.## CVXMOD 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.## CVXMOD 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# this program. If not, see <http://www.gnu.org/licenses/>.from base import *# user importsimport cvxopt.basedef eval(obj):    return cvxopt.base.exp(obj)class functionalform(function, elementwise, convex, increasing, positive):    """Understands exp(x)."""    def __init__(self, arg):        self.arg = arg        self.rows = rows(arg)        self.cols = cols(arg)class _stdform1(object):    # inherit from something, later? jem. include NotImplementedError errors and a    # test() function or so.    """An F() standard form for exp(x) - t <= 0."""    def __init__(self, x, t):        # jem some nasty hardcoding here.        self.rows = rows(x)        self.cols = 1        self.optvars = set((x, t))        self.x = x        self.t = t    def indomain(self):        return True    def setindomain(self):        self.x.value = ones(size(self.x))        self.t.value = 2*ones(size(self.t))    def value(self):        return eval(value(self.x)) - value(self.t)    def jacobian(self, var):        # not *quite* the jacobian, but nearly.        if var is self.x:            return diag(cvxopt.base.exp(value(self.x)))        elif var is self.t:            return -eye(rows(self.t))        else:            raise OptimizationError('illegal jacobian')    def hessianz(self, firstvar, secondvar, z):        if firstvar is secondvar is self.x:            return diag(diag(cvxopt.base.exp(value(self.x)))*z)        elif firstvar is secondvar is self.t:            return zeros(rows(self.t))        elif firstvar is self.x and secondvar is self.t:            return zeros(rows(self.x), rows(self.t))        elif firstvar is self.t and secondvar is self.x:            return zeros(rows(self.t), rows(self.x))        else:            raise OptimizationError('illegal hessian')applystdform = stdconvex(functionalform, _stdform1)