there is no row/column ambiguity::
>>> import cvxopt
- >>> import numpy
>>> from dunshire import *
>>> K = NonnegativeOrthant(2)
>>> L = [[1,0],[0,1]]
>>> e1 = cvxopt.matrix([1,1])
- >>> e2 = numpy.matrix([1,1])
+ >>> e2 = (1,1)
>>> G = SymmetricLinearGame(L, K, e1, e2)
>>> print(G)
The linear game (L, K, e1, e2) where
def tolerance_scale(self, solution):
r"""
- Return a scaling factor that should be applied to :const:`ABS_TOL`
- for this game.
+
+ Return a scaling factor that should be applied to
+ :const:`dunshire.options.ABS_TOL` for this game.
When performing certain comparisons, the default tolerance
- :const:`ABS_TOL` may not be appropriate. For example, if we expect
- ``x`` and ``y`` to be within :const:`ABS_TOL` of each other,
- than the inner product of ``L*x`` and ``y`` can be as far apart
- as the spectral norm of ``L`` times the sum of the norms of
- ``x`` and ``y``. Such a comparison is made in :meth:`solution`,
- and in many of our unit tests.
+ :const:`dunshire.options.ABS_TOL` may not be appropriate. For
+ example, if we expect ``x`` and ``y`` to be within
+ :const:`dunshire.options.ABS_TOL` of each other, than the inner
+ product of ``L*x`` and ``y`` can be as far apart as the spectral
+ norm of ``L`` times the sum of the norms of ``x`` and
+ ``y``. Such a comparison is made in :meth:`solution`, and in
+ many of our unit tests.
The returned scaling factor found from the inner product
mentioned above is
-------
float
- A scaling factor to be multiplied by :const:`ABS_TOL` when
+ A scaling factor to be multiplied by
+ :const:`dunshire.options.ABS_TOL` when
making comparisons involving solutions of this game.
Examples
Returns
-------
- :class:`Solution`
+ Solution
A :class:`Solution` object describing the game's value and
the optimal strategies of both players.