#18536: Solvers for constant sum games
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       Reporter:  ptigwe             |        Owner:
           Type:  enhancement        |       Status:  needs_work
       Priority:  minor              |    Milestone:  sage-6.8
      Component:  game theory        |   Resolution:
       Keywords:  Game Theory,       |    Merged in:
  Gambit, Zero-sum game Constant     |    Reviewers:  Karl-Dieter Crisman
  Sum Game, Normal Form Games        |  Work issues:
        Authors:  Tobenna P. Igwe    |       Commit:
Report Upstream:  N/A                |  60efdc7f823c8aa64ac176addcd376f1f571b09d
         Branch:                     |     Stopgaps:
  u/ptigwe/gt_extension              |
   Dependencies:                     |
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Comment (by ptigwe):

 > > I included it again just in case if the `_solve_gambit_LP` function
 was called externally without going through `_solve_LP`. If there is no
 need to retest just for this reason, then I'm happy to take it out.
 > Well, usually such underscore methods aren't "publicly" available.
 Vince, what do you think?
 Actually, gambit performs it's own check as well, which makes three checks
 in total. So I've removed the one within `_solve_gambit_LP`, and added a
 doctest for the gambit error.
 > > > * `return c.numpy().max() == c.numpy().min()` - is there no way to
 do this without using/importing `numpy`?  It would be nice to not have to
 use it - or is it slower to use Sage proper?
 > > Currently, this compares all entries of the matrix and makes sure it
 is within `sys.float_info.epsilon` of the first element.
 > >
 
||[http://git.sagemath.org/sage.git/commit/?id=d9571793d208ed772fb8e7a7a335f9934dda1fe8
 d957179]||{{{Update check for constant sum 'is_constant_sum'}}}||
 > But which one is in principle faster or better?  I don't mind using
 numpy as long as importing it doesn't cause problems in speed, if it's
 better (which perhaps it is).
 The current implementation is faster than the numpy implementation. I ran
 some bench marks using `timeit` and got `177 µs per loop` for the current
 implementation vs `992 ms per loop` for the numpy implementation using a
 matrix of size `1000x1000`.
 > > Currently, we are considering moving the `maximization` option into
 the constructor of the class.
 > Fine, but this is currently breaking functionality.  So either you have
 to do that here, or make that change a prereq to this ticket, or something
 else.  My recommendation is to just leave it here for now and then deal
 with the class constructor bit in a separate ticket (to make things as
 orthogonal as possible).
 Integrated the `maximization` parameter into `_as_gambit_game`.

--
Ticket URL: <http://trac.sagemath.org/ticket/18536#comment:10>
Sage <http://www.sagemath.org>
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