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gnm_solve/gambit-gnm now correctly handles the degenerate case of a game where all
payoffs are the same (#405), and checks that the perturbation vector specified has at least
one non-zero component (#194)
ipa_solve/gambit-ipa ensures the use of a generic perturbation vector; this resolves a
problem where the method could return non-Nash output (#406)
gambit-enumpoly could get stuck in an infinite loop, and/or fail to report some equilibria,
due to floating-point rounding/tolerance issues; this has been fixed on known cases (#198)
gambit-logit now uses perturbations to attempt to resolve correspondences that have
bifurcations, and instead tries always to follow a curve that has the same orientation.
This should eliminate cases in which tracing gets stuck in a loop or reverses itself
when encountering bifurcations (#3)
Added
MixedStrategyProfile and MixedBehaviorProfile objects in pygambit can now be iterated in
various dict-like ways
gnm_solve/gambit-gnm now exposes several parameters which control the behavior of the
path-following procedure
The MixedBehaviorProfile object can now be initialized on creation by a given distribution.
append_move/append_infoset now resolves either a singleton node reference or any
iterable set of node references
Additional regret-related functions added to MixedBehaviorProfile and MixedStrategyProfile
in both C++ and Python
Some caching added to payoff/strategy value calculations in MixedStrategyProfile
gambit-simpdiv now supports expressing output as floating-point with a specified number of
digits (#296)
Parameters first_step and max_accel added to gambit_logit for finer control of
numerical continuation process
Changed
Gambit now requires a compiler that supports C++17.
Functions to compute Nash equilibria now return a NashComputationResult object instead of a bare
list of profiles (#190)
liap_solve/gambit-liap has been reimplemented to scale payoffs uniformly across games,
to always take an explicit starting point (in liap_solve), and to specify a regret-based
acceptance criterion (#330)
simpdiv_solve/gambit-simpdiv now accepts a regret-based acceptance criterion (#439)
simpdiv_solve now takes an explicit starting point (#445)
Converted test suite for mixed behavior profiles to pytest style; added parametrizations for
test_realiz_prob; added test_martingale_property_of_node_value (#375)
Improved test suite for mixed strategy profiles (#374)
Test suite for pygambit moved from src/pygambit/tests/ to tests/
Improved repr methods in pygambit for game-related classes
Further extension of test suite for mixed behavior profiles to cover new indexing and profile
order consistency for payoff-related calculations
Overhaul of caching in MixedBehaviorProfile to use maps (std::map)
Creation of random mixed profiles in pygambit is done with new Game.random_strategy_profile and Game.random_behavior_profile methods; these accept numpy.random.Generator objects for
reproducible state.
Creation of random mixed profiles in C++ is done with new Game::NewRandomStrategyProfile and Game::NewRandomBehaviorProfile methods; these accept STL Generator objects for reproducible state.
The Python implementation is no longer just a wrapper around the C++ one.
Graphical interface now uses simplicial subdivision as the recommended method for finding some
equilibria in games with more than two players, instead of Lyapunov function minimisation