1# ------------------------------------------------------------------------------ 

2# Copyright (C) 2019 Maximilian Stahlberg 

3# Based on the original picos.expressions module by Guillaume Sagnol. 

4# 

5# This file is part of PICOS. 

6# 

7# PICOS is free software: you can redistribute it and/or modify it under the 

8# terms of the GNU General Public License as published by the Free Software 

9# Foundation, either version 3 of the License, or (at your option) any later 

10# version. 

11# 

12# PICOS is distributed in the hope that it will be useful, but WITHOUT ANY 

13# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR 

14# A PARTICULAR PURPOSE. See the GNU General Public License for more details. 

15# 

16# You should have received a copy of the GNU General Public License along with 

17# this program. If not, see <http://www.gnu.org/licenses/>. 

18# ------------------------------------------------------------------------------ 

19 

20"""Implements :class:`Logarithm`.""" 

21 

22import math 

23import operator 

24from collections import namedtuple 

25 

26import cvxopt 

27 

28from .. import glyphs 

29from ..apidoc import api_end, api_start 

30from ..constraints import LogConstraint 

31from .data import convert_and_refine_arguments, convert_operands 

32from .exp_affine import AffineExpression 

33from .expression import Expression, refine_operands, validate_prediction 

34 

35_API_START = api_start(globals()) 

36# ------------------------------- 

37 

38 

39class Logarithm(Expression): 

40 r"""Logarithm of a scalar affine expression. 

41 

42 :Definition: 

43 

44 For a real scalar affine expression :math:`x`, this is :math:`\log(x)`. 

45 

46 .. warning:: 

47 

48 When you pose a lower bound on a logarithm :math:`\log(x)`, then PICOS 

49 enforces :math:`x \geq 0` through an auxiliary constraint during 

50 solution search. 

51 """ 

52 

53 # -------------------------------------------------------------------------- 

54 # Initialization and factory methods. 

55 # -------------------------------------------------------------------------- 

56 

57 @convert_and_refine_arguments("x") 

58 def __init__(self, x): 

59 """Construct a :class:`Logarithm`. 

60 

61 :param x: The scalar affine expression :math:`x`. 

62 :type x: ~picos.expressions.AffineExpression 

63 """ 

64 if not isinstance(x, AffineExpression): 

65 raise TypeError("Can only take the logarithm of a real affine " 

66 "expression, not of {}.".format(type(x).__name__)) 

67 elif not x.scalar: 

68 raise TypeError("Can only take the logarithm of a scalar expression" 

69 "but {} is shaped {}.".format(x.string, glyphs.shape(x.shape))) 

70 

71 self._x = x 

72 

73 Expression.__init__(self, "Logarithm", glyphs.log(x.string)) 

74 

75 # -------------------------------------------------------------------------- 

76 # Abstract method implementations and method overridings, except _predict. 

77 # -------------------------------------------------------------------------- 

78 

79 def _get_refined(self): 

80 if self._x.constant: 

81 return AffineExpression.from_constant(self.value, 1, self._symbStr) 

82 else: 

83 return self 

84 

85 Subtype = namedtuple("Subtype", ()) 

86 

87 def _get_subtype(self): 

88 return self.Subtype() 

89 

90 def _get_value(self): 

91 value = cvxopt.matrix(self._x._get_value()) # Must be dense for log. 

92 return cvxopt.log(value) 

93 

94 def _get_mutables(self): 

95 return self._x._get_mutables() 

96 

97 def _is_convex(self): 

98 return False 

99 

100 def _is_concave(self): 

101 return True 

102 

103 def _replace_mutables(self, mapping): 

104 return self.__class__(self._x._replace_mutables(mapping)) 

105 

106 def _freeze_mutables(self, freeze): 

107 return self.__class__(self._x._freeze_mutables(freeze)) 

108 

109 # -------------------------------------------------------------------------- 

110 # Python special method implementations, except constraint-creating ones. 

111 # -------------------------------------------------------------------------- 

112 

113 @convert_operands(scalarRHS=True) 

114 @refine_operands() 

115 def __add__(self, other): 

116 if isinstance(other, AffineExpression): 

117 if not other.constant: 

118 raise NotImplementedError("You may only add a constant term to " 

119 "a nonconstant PICOS logarithm.") 

120 

121 log = Logarithm(self._x * math.exp(other.value)) 

122 log._typeStr = "Offset " + log._typeStr 

123 log._symbStr = glyphs.clever_add(self.string, other.string) 

124 

125 return log 

126 

127 @convert_operands(scalarRHS=True) 

128 @refine_operands() 

129 def __radd__(self, other): 

130 if isinstance(other, AffineExpression): 

131 log = self.__add__(other) 

132 # NOTE: __add__ always creates a fresh expression. 

133 log._symbStr = glyphs.clever_add(other.string, self.string) 

134 return log 

135 else: 

136 return NotImplemented 

137 

138 @convert_operands(scalarRHS=True) 

139 @refine_operands() 

140 def __sub__(self, other): 

141 if isinstance(other, AffineExpression): 

142 if not other.constant: 

143 raise NotImplementedError("You may only substract a constant " 

144 "term from a nonconstant PICOS logarithm.") 

145 

146 log = Logarithm(self._x / math.exp(other.value)) 

147 log._typeStr = "Offset " + log._typeStr 

148 log._symbStr = glyphs.clever_sub(self.string, other.string) 

149 

150 return log 

151 

152 @convert_operands(scalarRHS=True) 

153 @refine_operands() 

154 def __mul__(self, other): 

155 from . import Entropy, NegativeEntropy 

156 

157 if isinstance(other, AffineExpression): 

158 if other.is0: 

159 return AffineExpression.zero() 

160 elif other.is1: 

161 # NOTE: We could return self here, but this is more consistent 

162 # with other expressions' __mul__ methods. 

163 return Logarithm(self._x) 

164 elif other.equals(self._x): 

165 return NegativeEntropy(self._x) 

166 elif other.equals(-self._x): 

167 return Entropy(self._x) 

168 else: 

169 raise NotImplementedError( 

170 "You may multiply {} only with {}, 0, or 1." 

171 .format(self.string, glyphs.plsmns(self._x.string))) 

172 else: 

173 return NotImplemented 

174 

175 @convert_operands(scalarRHS=True) 

176 @refine_operands() 

177 def __rmul__(self, other): 

178 if isinstance(other, AffineExpression): 

179 return self.__mul__(other) 

180 else: 

181 return NotImplemented 

182 

183 # -------------------------------------------------------------------------- 

184 # Methods and properties that return expressions. 

185 # -------------------------------------------------------------------------- 

186 

187 @property 

188 def x(self): 

189 """The expression :math:`x`.""" 

190 return self._x 

191 

192 @property 

193 def exp(self): 

194 """The exponential of the logarithm, equal to :math:`x`.""" 

195 return self._x 

196 

197 # -------------------------------------------------------------------------- 

198 # Constraint-creating operators, and _predict. 

199 # -------------------------------------------------------------------------- 

200 

201 @classmethod 

202 def _predict(cls, subtype, relation, other): 

203 assert isinstance(subtype, cls.Subtype) 

204 

205 if relation == operator.__ge__: 

206 if issubclass(other.clstype, AffineExpression) \ 

207 and other.subtype.dim == 1: 

208 return LogConstraint.make_type() 

209 

210 return NotImplemented 

211 

212 @convert_operands(scalarRHS=True) 

213 @validate_prediction 

214 @refine_operands() 

215 def __ge__(self, other): 

216 if isinstance(other, AffineExpression): 

217 return LogConstraint(self, other) 

218 else: 

219 return NotImplemented 

220 

221 

222# -------------------------------------- 

223__all__ = api_end(_API_START, globals())