X-Git-Url: http://gitweb.michael.orlitzky.com/?a=blobdiff_plain;f=src%2Fdunshire%2Fgames.py;h=65fc791b20bd0624ee714a724581eb1537fa2edf;hb=a5ecd81c2b98bb38bfee58507f1b4360033ad480;hp=4b5383dd2f12c1fe2d6de250760e5009639bfd5d;hpb=4f0bc24d81ecbce9b2220d042b407529917ef5c6;p=dunshire.git diff --git a/src/dunshire/games.py b/src/dunshire/games.py index 4b5383d..65fc791 100644 --- a/src/dunshire/games.py +++ b/src/dunshire/games.py @@ -14,7 +14,7 @@ from unittest import TestCase from cvxopt import matrix, printing, solvers from cones import CartesianProduct, IceCream, NonnegativeOrthant from errors import GameUnsolvableException -from matrices import append_col, append_row, identity, inner_product +from matrices import append_col, append_row, identity, inner_product, norm import options printing.options['dformat'] = options.FLOAT_FORMAT @@ -433,21 +433,35 @@ class SymmetricLinearGame: # what happened. soln_dict = solvers.conelp(c, G, h, C.cvxopt_dims(), A, b) + p1_value = -soln_dict['primal objective'] + p2_value = -soln_dict['dual objective'] + p1_optimal = soln_dict['x'][1:] + p2_optimal = soln_dict['z'][self._K.dimension():] + # 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': + # infeasible", "dual infeasible", "unknown", all of which mean + # we didn't get a solution. The "infeasible" ones are the + # worst, since they indicate that CVXOPT is convinced the + # problem is infeasible (and that cannot happen). + if soln_dict['status'] in ['primal infeasible', 'dual infeasible']: raise GameUnsolvableException(soln_dict) - - p1_value = soln_dict['x'][0] - p1_optimal = soln_dict['x'][1:] - p2_optimal = soln_dict['z'][self._K.dimension():] + elif soln_dict['status'] == 'unknown': + # When we get a status of "unknown", we may still be able + # to salvage a solution out of the returned + # dictionary. Often this is the result of numerical + # difficulty and we can simply check that the primal/dual + # objectives match (within a tolerance) and that the + # primal/dual optimal solutions are within the cone (to a + # tolerance as well). + if abs(p1_value - p2_value) > options.ABS_TOL: + raise GameUnsolvableException(soln_dict) + if (p1_optimal not in self._K) or (p2_optimal not in self._K): + raise GameUnsolvableException(soln_dict) return Solution(p1_value, p1_optimal, p2_optimal) + def dual(self): r""" Return the dual game to this game. @@ -489,11 +503,79 @@ class SymmetricLinearGame: self._e1) + +def _random_matrix(dims): + """ + Generate a random square (``dims``-by-``dims``) matrix, + represented as a list of rows. This is used only by the + :class:`SymmetricLinearGameTest` class. + """ + return [[uniform(-10, 10) for i in range(dims)] for j in range(dims)] + +def _random_nonnegative_matrix(dims): + """ + Generate a random square (``dims``-by-``dims``) matrix with + nonnegative entries, represented as a list of rows. This is used + only by the :class:`SymmetricLinearGameTest` class. + """ + L = _random_matrix(dims) + return [[abs(entry) for entry in row] for row in L] + +def _random_diagonal_matrix(dims): + """ + Generate a random square (``dims``-by-``dims``) matrix with nonzero + entries only on the diagonal, represented as a list of rows. This is + used only by the :class:`SymmetricLinearGameTest` class. + """ + return [[uniform(-10, 10)*int(i == j) for i in range(dims)] + for j in range(dims)] + +def _random_orthant_params(): + """ + Generate the ``L``, ``K``, ``e1``, and ``e2`` parameters for a + random game over the nonnegative orthant. This is only used by + the :class:`SymmetricLinearGameTest` class. + """ + ambient_dim = randint(1, 10) + K = NonnegativeOrthant(ambient_dim) + e1 = [uniform(0.5, 10) for idx in range(K.dimension())] + e2 = [uniform(0.5, 10) for idx in range(K.dimension())] + L = _random_matrix(K.dimension()) + return (L, K, e1, e2) + + +def _random_icecream_params(): + """ + Generate the ``L``, ``K``, ``e1``, and ``e2`` parameters for a + random game over the ice cream cone. This is only used by + the :class:`SymmetricLinearGameTest` class. + """ + # 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] # Set the "height" of e1 to one + e2 = [1] # And the same for e2 + + # 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 = _random_matrix(K.dimension()) + + return (L, K, e1, e2) + + 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 @@ -502,6 +584,15 @@ class SymmetricLinearGameTest(TestCase): self.assertTrue(abs(first - second) < options.ABS_TOL) + def assert_norm_within_tol(self, first, second): + """ + Test that ``first`` and ``second`` vectors are equal in the + sense that the norm of their difference is within our default + tolerance. + """ + self.assert_within_tol(norm(first - second), 0) + + def assert_solution_exists(self, L, K, e1, e2): """ Given the parameters needed to construct a SymmetricLinearGame, @@ -509,12 +600,17 @@ class SymmetricLinearGameTest(TestCase): """ G = SymmetricLinearGame(L, K, e1, e2) soln = G.solution() + + # The matrix() constructor assumes that ``L`` is a list of + # columns, so we transpose it to agree with what + # SymmetricLinearGame() thinks. 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): + + def test_solution_exists_orthant(self): """ Every linear game has a solution, so we should be able to solve every symmetric linear game over the NonnegativeOrthant. Pick @@ -522,105 +618,223 @@ class SymmetricLinearGameTest(TestCase): 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())] + (L, K, e1, e2) = _random_orthant_params() self.assert_solution_exists(L, K, e1, e2) - def test_solution_exists_ice_cream(self): + + def test_solution_exists_icecream(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] # Set the "height" of e1 to one - e2 = [1] # And the same for e2 - - # 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())] + (L, K, e1, e2) = _random_icecream_params() self.assert_solution_exists(L, K, e1, e2) - def test_negative_value_Z_operator(self): + def test_negative_value_z_operator(self): """ Test the example given in Gowda/Ravindran of a Z-matrix with negative game value on the nonnegative orthant. """ K = NonnegativeOrthant(2) - e1 = [1,1] + e1 = [1, 1] e2 = e1 - L = [[1,-2],[-2,1]] + L = [[1, -2], [-2, 1]] G = SymmetricLinearGame(L, K, e1, e2) self.assertTrue(G.solution().game_value() < -options.ABS_TOL) - def test_nonnegative_scaling_orthant(self): + def assert_scaling_works(self, L, K, e1, e2): """ Test that scaling ``L`` by a nonnegative number scales the value - of the game by the same number. Use the nonnegative orthant as - our cone. - """ - 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 = matrix([[uniform(-10, 10) for i in range(K.dimension())] - for j in range(K.dimension())]) - G1 = SymmetricLinearGame(L, K, e1, e2) - value1 = G1.solution().game_value() - alpha = uniform(0.1, 10) + of the game by the same number. + """ + # Make ``L`` a matrix so that we can scale it by alpha. Its + # random, so who cares if it gets transposed. + L = matrix(L) + game1 = SymmetricLinearGame(L, K, e1, e2) + value1 = game1.solution().game_value() - G2 = SymmetricLinearGame(alpha*L, K, e1, e2) - value2 = G2.solution().game_value() + alpha = uniform(0.1, 10) + game2 = SymmetricLinearGame(alpha*L, K, e1, e2) + value2 = game2.solution().game_value() self.assert_within_tol(alpha*value1, value2) - def test_nonnegative_scaling_icecream(self): + def test_scaling_orthant(self): + """ + Test that scaling ``L`` by a nonnegative number scales the value + of the game by the same number over the nonnegative orthant. + """ + (L, K, e1, e2) = _random_orthant_params() + self.assert_scaling_works(L, K, e1, e2) + + + def test_scaling_icecream(self): """ The same test as :meth:`test_nonnegative_scaling_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] # Set the "height" of e1 to one - e2 = [1] # And the same for e2 - - # 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 = matrix([[uniform(-10, 10) for i in range(K.dimension())] - for j in range(K.dimension())]) - - G1 = SymmetricLinearGame(L, K, e1, e2) - value1 = G1.solution().game_value() - alpha = uniform(0.1, 10) + (L, K, e1, e2) = _random_icecream_params() + self.assert_scaling_works(L, K, e1, e2) - G2 = SymmetricLinearGame(alpha*L, K, e1, e2) - value2 = G2.solution().game_value() - self.assert_within_tol(alpha*value1, value2) + def assert_translation_works(self, L, K, e1, e2): + """ + Check that translating ``L`` by alpha*(e1*e2.trans()) increases + the value of the associated game by alpha. + """ + e1 = matrix(e1, (K.dimension(), 1)) + e2 = matrix(e2, (K.dimension(), 1)) + game1 = SymmetricLinearGame(L, K, e1, e2) + soln1 = game1.solution() + value1 = soln1.game_value() + x_bar = soln1.player1_optimal() + y_bar = soln1.player2_optimal() + + # Make ``L`` a CVXOPT matrix so that we can do math with + # it. Note that this gives us the "correct" representation of + # ``L`` (in agreement with what G has), but COLUMN indexed. + alpha = uniform(-10, 10) + L = matrix(L).trans() + tensor_prod = e1*e2.trans() + + # Likewise, this is the "correct" representation of ``M``, but + # COLUMN indexed... + M = L + alpha*tensor_prod + + # so we have to transpose it when we feed it to the constructor. + game2 = SymmetricLinearGame(M.trans(), K, e1, e2) + value2 = game2.solution().game_value() + + self.assert_within_tol(value1 + alpha, value2) + + # Make sure the same optimal pair works. + self.assert_within_tol(value2, inner_product(M*x_bar, y_bar)) + + + def test_translation_orthant(self): + """ + Test that translation works over the nonnegative orthant. + """ + (L, K, e1, e2) = _random_orthant_params() + self.assert_translation_works(L, K, e1, e2) + + + def test_translation_icecream(self): + """ + The same as :meth:`test_translation_orthant`, except over the + ice cream cone. + """ + (L, K, e1, e2) = _random_icecream_params() + self.assert_translation_works(L, K, e1, e2) + + + def assert_opposite_game_works(self, L, K, e1, e2): + """ + Check the value of the "opposite" game that gives rise to a + value that is the negation of the original game. Comes from + some corollary. + """ + e1 = matrix(e1, (K.dimension(), 1)) + e2 = matrix(e2, (K.dimension(), 1)) + game1 = SymmetricLinearGame(L, K, e1, e2) + + # Make ``L`` a CVXOPT matrix so that we can do math with + # it. Note that this gives us the "correct" representation of + # ``L`` (in agreement with what G has), but COLUMN indexed. + L = matrix(L).trans() + + # Likewise, this is the "correct" representation of ``M``, but + # COLUMN indexed... + M = -L.trans() + + # so we have to transpose it when we feed it to the constructor. + game2 = SymmetricLinearGame(M.trans(), K, e2, e1) + + soln1 = game1.solution() + x_bar = soln1.player1_optimal() + y_bar = soln1.player2_optimal() + soln2 = game2.solution() + + self.assert_within_tol(-soln1.game_value(), soln2.game_value()) + + # Make sure the switched optimal pair works. + self.assert_within_tol(soln2.game_value(), + inner_product(M*y_bar, x_bar)) + + + def test_opposite_game_orthant(self): + """ + Test the value of the "opposite" game over the nonnegative + orthant. + """ + (L, K, e1, e2) = _random_orthant_params() + self.assert_opposite_game_works(L, K, e1, e2) + + + def test_opposite_game_icecream(self): + """ + Like :meth:`test_opposite_game_orthant`, except over the + ice-cream cone. + """ + (L, K, e1, e2) = _random_icecream_params() + self.assert_opposite_game_works(L, K, e1, e2) + + + def assert_orthogonality(self, L, K, e1, e2): + """ + Two orthogonality relations hold at an optimal solution, and we + check them here. + """ + game = SymmetricLinearGame(L, K, e1, e2) + soln = game.solution() + x_bar = soln.player1_optimal() + y_bar = soln.player2_optimal() + value = soln.game_value() + + # Make these matrices so that we can compute with them. + L = matrix(L).trans() + e1 = matrix(e1, (K.dimension(), 1)) + e2 = matrix(e2, (K.dimension(), 1)) + + ip1 = inner_product(y_bar, L*x_bar - value*e1) + self.assert_within_tol(ip1, 0) + + ip2 = inner_product(value*e2 - L.trans()*y_bar, x_bar) + self.assert_within_tol(ip2, 0) + + + def test_orthogonality_orthant(self): + """ + Check the orthgonality relationships that hold for a solution + over the nonnegative orthant. + """ + (L, K, e1, e2) = _random_orthant_params() + self.assert_orthogonality(L, K, e1, e2) + + + def test_orthogonality_icecream(self): + """ + Check the orthgonality relationships that hold for a solution + over the ice-cream cone. + """ + (L, K, e1, e2) = _random_icecream_params() + self.assert_orthogonality(L, K, e1, e2) + + + def test_positive_operator_value(self): + """ + Test that a positive operator on the nonnegative orthant gives + rise to a a game with a nonnegative value. + + This test theoretically applies to the ice-cream cone as well, + but we don't know how to make positive operators on that cone. + """ + (_, K, e1, e2) = _random_orthant_params() + + # Ignore that L, we need a nonnegative one. + L = _random_nonnegative_matrix(K.dimension()) + + game = SymmetricLinearGame(L, K, e1, e2) + self.assertTrue(game.solution().game_value() >= -options.ABS_TOL)