]> gitweb.michael.orlitzky.com - dunshire.git/blobdiff - src/dunshire/symmetric_linear_game.py
Add a unit test for the ice cream cone solution.
[dunshire.git] / src / dunshire / symmetric_linear_game.py
index e92e8200ad93e97d76b61f3453d40649fcdadcc0..7be55d76ffb36e058b27b85b82dd9caecf2971c5 100644 (file)
@@ -5,14 +5,20 @@ This module contains the main SymmetricLinearGame class that knows how
 to solve a linear game.
 """
 
-from cvxopt import matrix, printing, solvers
+# These few are used only for tests.
+from math import sqrt
+from random import randint, uniform
+from unittest import TestCase
 
-from cones import CartesianProduct
+# These are mostly actually needed.
+from cvxopt import matrix, printing, solvers
+from cones import CartesianProduct, IceCream, NonnegativeOrthant
 from errors import GameUnsolvableException
-from matrices import append_col, append_row, identity
+from matrices import append_col, append_row, identity, inner_product
+import options
 
-printing.options['dformat'] = '%.7f'
-solvers.options['show_progress'] = False
+printing.options['dformat'] = options.FLOAT_FORMAT
+solvers.options['show_progress'] = options.VERBOSE
 
 
 class Solution:
@@ -21,6 +27,10 @@ class Solution:
     the value of the game, and both players' strategies.
     """
     def __init__(self, game_value, p1_optimal, p2_optimal):
+        """
+        Create a new Solution object from a game value and two optimal
+        strategies for the players.
+        """
         self._game_value = game_value
         self._player1_optimal = p1_optimal
         self._player2_optimal = p2_optimal
@@ -35,11 +45,21 @@ class Solution:
           * The optimal strategy of player one.
           * The optimal strategy of player two.
 
-        """
+        EXAMPLES:
+
+           >>> print(Solution(10, matrix([1,2]), matrix([3,4])))
+           Game value: 10.0000000
+           Player 1 optimal:
+             [ 1]
+             [ 2]
+           Player 2 optimal:
+             [ 3]
+             [ 4]
 
+        """
         tpl = 'Game value: {:.7f}\n' \
               'Player 1 optimal:{:s}\n' \
-              'Player 2 optimal:{:s}\n'
+              'Player 2 optimal:{:s}'
 
         p1_str = '\n{!s}'.format(self.player1_optimal())
         p1_str = '\n  '.join(p1_str.splitlines())
@@ -50,14 +70,23 @@ class Solution:
 
 
     def game_value(self):
+        """
+        Return the game value for this solution.
+        """
         return self._game_value
 
 
     def player1_optimal(self):
+        """
+        Return player one's optimal strategy in this solution.
+        """
         return self._player1_optimal
 
 
     def player2_optimal(self):
+        """
+        Return player two's optimal strategy in this solution.
+        """
         return self._player2_optimal
 
 
@@ -79,27 +108,110 @@ class SymmetricLinearGame:
 
     The ambient space is assumed to be the span of ``K``.
     """
-
     def __init__(self, L, K, e1, e2):
         """
         INPUT:
 
-          - ``L`` -- an n-by-b matrix represented as a list of lists
-             of real numbers.
+          - ``L`` -- an square matrix represented as a list of lists
+             of real numbers. ``L`` itself is interpreted as a list of
+             ROWS, which agrees with (for example) SageMath and NumPy,
+             but not with CVXOPT (whose matrix constructor accepts a
+             list of columns).
 
           - ``K`` -- a SymmetricCone instance.
 
-          - ``e1`` -- the interior point of ``K`` belonging to player one,
-                      as a column vector.
-
-          - ``e2`` -- the interior point of ``K`` belonging to player two,
-                      as a column vector.
-
+          - ``e1`` -- the interior point of ``K`` belonging to player one;
+            it can be of any enumerable type having the correct length.
+
+          - ``e2`` -- the interior point of ``K`` belonging to player two;
+            it can be of any enumerable type having the correct length.
+
+        EXAMPLES:
+
+        Lists can (and probably should) be used for every argument:
+
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(2)
+            >>> L = [[1,0],[0,1]]
+            >>> e1 = [1,1]
+            >>> e2 = [1,1]
+            >>> G = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(G)
+            The linear game (L, K, e1, e2) where
+              L = [ 1  0]
+                  [ 0  1],
+              K = Nonnegative orthant in the real 2-space,
+              e1 = [ 1]
+                   [ 1],
+              e2 = [ 1]
+                   [ 1].
+
+        The points ``e1`` and ``e2`` can also be passed as some other
+        enumerable type (of the correct length) without much harm, since
+        there is no row/column ambiguity:
+
+            >>> import cvxopt
+            >>> import numpy
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(2)
+            >>> L = [[1,0],[0,1]]
+            >>> e1 = cvxopt.matrix([1,1])
+            >>> e2 = numpy.matrix([1,1])
+            >>> G = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(G)
+            The linear game (L, K, e1, e2) where
+              L = [ 1  0]
+                  [ 0  1],
+              K = Nonnegative orthant in the real 2-space,
+              e1 = [ 1]
+                   [ 1],
+              e2 = [ 1]
+                   [ 1].
+
+        However, ``L`` will always be intepreted as a list of rows, even
+        if it is passed as a ``cvxopt.base.matrix`` which is otherwise
+        indexed by columns:
+
+            >>> import cvxopt
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(2)
+            >>> L = [[1,2],[3,4]]
+            >>> e1 = [1,1]
+            >>> e2 = e1
+            >>> G = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(G)
+            The linear game (L, K, e1, e2) where
+              L = [ 1  2]
+                  [ 3  4],
+              K = Nonnegative orthant in the real 2-space,
+              e1 = [ 1]
+                   [ 1],
+              e2 = [ 1]
+                   [ 1].
+            >>> L = cvxopt.matrix(L)
+            >>> print(L)
+            [ 1  3]
+            [ 2  4]
+            <BLANKLINE>
+            >>> G = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(G)
+            The linear game (L, K, e1, e2) where
+              L = [ 1  2]
+                  [ 3  4],
+              K = Nonnegative orthant in the real 2-space,
+              e1 = [ 1]
+                   [ 1],
+              e2 = [ 1]
+                   [ 1].
         """
         self._K = K
         self._e1 = matrix(e1, (K.dimension(), 1))
         self._e2 = matrix(e2, (K.dimension(), 1))
-        self._L = matrix(L, (K.dimension(), K.dimension()))
+
+        # Our input ``L`` is indexed by rows but CVXOPT matrices are
+        # indexed by columns, so we need to transpose the input before
+        # feeding it to CVXOPT.
+        self._L = matrix(L, (K.dimension(), K.dimension())).trans()
 
         if not K.contains_strict(self._e1):
             raise ValueError('the point e1 must lie in the interior of K')
@@ -107,19 +219,137 @@ class SymmetricLinearGame:
         if not K.contains_strict(self._e2):
             raise ValueError('the point e2 must lie in the interior of K')
 
+    def __str__(self):
+        """
+        Return a string representatoin of this game.
+
+        EXAMPLES:
+
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(3)
+            >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+            >>> e1 = [1,1,1]
+            >>> e2 = [1,2,3]
+            >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(SLG)
+            The linear game (L, K, e1, e2) where
+              L = [  1  -5 -15]
+                  [ -1   2  -3]
+                  [-12 -15   1],
+              K = Nonnegative orthant in the real 3-space,
+              e1 = [ 1]
+                   [ 1]
+                   [ 1],
+              e2 = [ 1]
+                   [ 2]
+                   [ 3].
+
+        """
+        tpl = 'The linear game (L, K, e1, e2) where\n' \
+              '  L = {:s},\n' \
+              '  K = {!s},\n' \
+              '  e1 = {:s},\n' \
+              '  e2 = {:s}.'
+        indented_L = '\n      '.join(str(self._L).splitlines())
+        indented_e1 = '\n       '.join(str(self._e1).splitlines())
+        indented_e2 = '\n       '.join(str(self._e2).splitlines())
+        return tpl.format(indented_L, str(self._K), indented_e1, indented_e2)
+
+
     def solution(self):
+        """
+        Solve this linear game and return a Solution object.
+
+        OUTPUT:
+
+        If the cone program associated with this game could be
+        successfully solved, then a Solution object containing the
+        game's value and optimal strategies is returned. If the game
+        could *not* be solved -- which should never happen -- then a
+        GameUnsolvableException is raised. It can be printed to get the
+        raw output from CVXOPT.
+
+        EXAMPLES:
+
+        This example is computed in Gowda and Ravindran in the section
+        "The value of a Z-transformation":
+
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(3)
+            >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+            >>> e1 = [1,1,1]
+            >>> e2 = [1,1,1]
+            >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(SLG.solution())
+            Game value: -6.1724138
+            Player 1 optimal:
+              [ 0.5517241]
+              [-0.0000000]
+              [ 0.4482759]
+            Player 2 optimal:
+              [0.4482759]
+              [0.0000000]
+              [0.5517241]
+
+        The value of the following game can be computed using the fact
+        that the identity is invertible:
+
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(3)
+            >>> L = [[1,0,0],[0,1,0],[0,0,1]]
+            >>> e1 = [1,2,3]
+            >>> e2 = [4,5,6]
+            >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(SLG.solution())
+            Game value: 0.0312500
+            Player 1 optimal:
+              [0.0312500]
+              [0.0625000]
+              [0.0937500]
+            Player 2 optimal:
+              [0.1250000]
+              [0.1562500]
+              [0.1875000]
+
+        """
+        # The cone "C" that appears in the statement of the CVXOPT
+        # conelp program.
         C = CartesianProduct(self._K, self._K)
+
+        # The column vector "b" that appears on the right-hand side of
+        # Ax = b in the statement of the CVXOPT conelp program.
         b = matrix([1], tc='d')
+
         # A column of zeros that fits K.
         zero = matrix(0, (self._K.dimension(), 1), tc='d')
+
+        # The column vector "h" that appears on the right-hand side of
+        # Gx + s = h in the statement of the CVXOPT conelp program.
         h = matrix([zero, zero])
+
+        # The column vector "c" that appears in the objective function
+        # value <c,x> in the statement of the CVXOPT conelp program.
         c = matrix([-1, zero])
+
+        # The matrix "G" that appears on the left-hand side of Gx + s = h
+        # in the statement of the CVXOPT conelp program.
         G = append_row(append_col(zero, -identity(self._K.dimension())),
                        append_col(self._e1, -self._L))
-        A = matrix([0, self._e1], (1, self._K.dimension() + 1), 'd')
 
+        # The matrix "A" that appears on the right-hand side of Ax = b
+        # in the statement of the CVXOPT conelp program.
+        A = matrix([0, self._e2], (1, self._K.dimension() + 1), 'd')
+
+        # Actually solve the thing and obtain a dictionary describing
+        # what happened.
         soln_dict = solvers.conelp(c, G, h, C.cvxopt_dims(), A, b)
 
+        # The "status" field contains "optimal" if everything went
+        # according to plan. Other possible values are "primal
+        # infeasible", "dual infeasible", "unknown", all of which
+        # mean we didn't get a solution. That should never happen,
+        # because by construction our game has a solution, and thus
+        # the cone program should too.
         if soln_dict['status'] != 'optimal':
             raise GameUnsolvableException(soln_dict)
 
@@ -128,3 +358,102 @@ class SymmetricLinearGame:
         p2_optimal = soln_dict['z'][self._K.dimension():]
 
         return Solution(p1_value, p1_optimal, p2_optimal)
+
+    def dual(self):
+        """
+        Return the dual game to this game.
+
+        EXAMPLES:
+
+            >>> from cones import NonnegativeOrthant
+            >>> K = NonnegativeOrthant(3)
+            >>> L = [[1,-5,-15],[-1,2,-3],[-12,-15,1]]
+            >>> e1 = [1,1,1]
+            >>> e2 = [1,2,3]
+            >>> SLG = SymmetricLinearGame(L, K, e1, e2)
+            >>> print(SLG.dual())
+            The linear game (L, K, e1, e2) where
+              L = [  1  -1 -12]
+                  [ -5   2 -15]
+                  [-15  -3   1],
+              K = Nonnegative orthant in the real 3-space,
+              e1 = [ 1]
+                   [ 2]
+                   [ 3],
+              e2 = [ 1]
+                   [ 1]
+                   [ 1].
+
+        """
+        return SymmetricLinearGame(self._L, # It will be transposed in __init__().
+                                   self._K, # Since "K" is symmetric.
+                                   self._e2,
+                                   self._e1)
+
+
+class SymmetricLinearGameTest(TestCase):
+    """
+    Tests for the SymmetricLinearGame and Solution classes.
+    """
+
+    def assert_within_tol(self, first, second):
+        """
+        Test that ``first`` and ``second`` are equal within our default
+        tolerance.
+        """
+        self.assertTrue(abs(first - second) < options.ABS_TOL)
+
+
+    def assert_solution_exists(self, L, K, e1, e2):
+        """
+        Given the parameters needed to construct a SymmetricLinearGame,
+        ensure that that game has a solution.
+        """
+        G = SymmetricLinearGame(L, K, e1, e2)
+        soln = G.solution()
+        L_matrix = matrix(L).trans()
+        expected = inner_product(L_matrix*soln.player1_optimal(),
+                                 soln.player2_optimal())
+        self.assert_within_tol(soln.game_value(), expected)
+
+    def test_solution_exists_nonnegative_orthant(self):
+        """
+        Every linear game has a solution, so we should be able to solve
+        every symmetric linear game over the NonnegativeOrthant. Pick
+        some parameters randomly and give it a shot. The resulting
+        optimal solutions should give us the optimal game value when we
+        apply the payoff operator to them.
+        """
+        ambient_dim = randint(1, 10)
+        K = NonnegativeOrthant(ambient_dim)
+        e1 = [uniform(0.1, 10) for idx in range(K.dimension())]
+        e2 = [uniform(0.1, 10) for idx in range(K.dimension())]
+        L = [[uniform(-10, 10) for i in range(K.dimension())]
+             for j in range(K.dimension())]
+        self.assert_solution_exists(L, K, e1, e2)
+
+    def test_solution_exists_ice_cream(self):
+        """
+        Like :meth:`test_solution_exists_nonnegative_orthant`, except
+        over the ice cream cone.
+        """
+        # Use a minimum dimension of two to avoid divide-by-zero in
+        # the fudge factor we make up later.
+        ambient_dim = randint(2, 10)
+        K = IceCream(ambient_dim)
+        e1 = [1]
+        e2 = [1]
+        # If we choose the rest of the components of e1,e2 randomly
+        # between 0 and 1, then the largest the squared norm of the
+        # non-height part of e1,e2 could be is the 1*(dim(K) - 1). We
+        # need to make it less than one (the height of the cone) so
+        # that the whole thing is in the cone. The norm of the
+        # non-height part is sqrt(dim(K) - 1), and we can divide by
+        # twice that.
+        fudge_factor = 1.0 / (2.0*sqrt(K.dimension() - 1.0))
+        e1 += [fudge_factor*uniform(0, 1) for idx in range(K.dimension() - 1)]
+        e2 += [fudge_factor*uniform(0, 1) for idx in range(K.dimension() - 1)]
+        L = [[uniform(-10, 10) for i in range(K.dimension())]
+             for j in range(K.dimension())]
+        self.assert_solution_exists(L, K, e1, e2)
+