From: Michael Orlitzky <michael@orlitzky.com>
Date: Fri, 4 Nov 2016 23:11:23 +0000 (-0400)
Subject: Fix a bug in my two-tolerance strategy and add a test case for it.
X-Git-Tag: 0.1.0~70
X-Git-Url: http://gitweb.michael.orlitzky.com/?p=dunshire.git;a=commitdiff_plain;h=eb940c4312540b7db2f43a8c4067aff6e94928de

Fix a bug in my two-tolerance strategy and add a test case for it.
---

diff --git a/dunshire/games.py b/dunshire/games.py
index 1f3a15a..08a6d6f 100644
--- a/dunshire/games.py
+++ b/dunshire/games.py
@@ -667,24 +667,48 @@ class SymmetricLinearGame:
             >>> norm(s1.player2_optimal() - s2.player2_optimal()) < ABS_TOL
             True
 
+        This game cannot be solved with the default tolerance, but it
+        can be solved with a weaker one::
+
+            >>> from dunshire import *
+            >>> from dunshire.options import ABS_TOL
+            >>> L = [[ 0.58538005706658102767,  1.53764301129883040886],
+            ...      [-1.34901059721452210027,  1.50121179114155500756]]
+            >>> K = NonnegativeOrthant(2)
+            >>> e1 = [1.04537193228494995623, 1.39699624965841895374]
+            >>> e2 = [0.35326554172108337593, 0.11795703527854853321]
+            >>> SLG = SymmetricLinearGame(L,K,e1,e2)
+            >>> print(SLG._try_solution(ABS_TOL / 10))
+            Traceback (most recent call last):
+            ...
+            dunshire.errors.GameUnsolvableException: Solution failed...
+            >>> print(SLG._try_solution(ABS_TOL))
+            Game value: 9.1100945
+            Player 1 optimal:
+              [-0.0000000]
+              [ 8.4776631]
+            Player 2 optimal:
+              [0.0000000]
+              [0.7158216]
+
         """
         try:
-            solvers.options['show_progress'] = options.VERBOSE
-            solvers.options['abs_tol'] = tolerance
+            opts = {'show_progress': options.VERBOSE, 'abstol': tolerance}
             soln_dict = solvers.conelp(self._c(),
                                        self._G(),
                                        self._h(),
                                        self._C().cvxopt_dims(),
                                        self._A(),
-                                       self._b())
-        except ValueError as e:
-            if str(e) == 'math domain error':
+                                       self._b(),
+                                       options=opts)
+        except ValueError as error:
+            if str(error) == 'math domain error':
                 # Oops, CVXOPT tried to take the square root of a
                 # negative number. Report some details about the game
                 # rather than just the underlying CVXOPT crash.
                 raise PoorScalingException(self)
             else:
-                raise e
+                raise error
 
         # The optimal strategies are named ``p`` and ``q`` in the
         # background documentation, and we need to extract them from