# \[ANN\] LogitNash.jl: Solving Games Faster Than GAMUT Can Generate Them

**URL:** <https://discourse.julialang.org/t/ann-logitnash-jl-solving-games-faster-than-gamut-can-generate-them/139825>\
**Category:** Package Announcements\
**Tags:** package, announcement, game-theory\
**Created:** [October 4, 2026, 11:54pm UTC](https://discourse.julialang.org/t/ann-logitnash-jl-solving-games-faster-than-gamut-can-generate-them/139825 "2026-10-04T23:54:13Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![votroto](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/votroto/32/224436_2.png) [@votroto](https://discourse.julialang.org/u/votroto)\
**Post date:** [October 4, 2026, 11:54pm UTC](https://discourse.julialang.org/t/ann-logitnash-jl-solving-games-faster-than-gamut-can-generate-them/139825/1 "2026-10-04T23:54:13Z")

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Hello lovely people,  
I would like to announce LogitNash.jl – a solver for finite multiplayer general-sum games, with the _slightly_ provocative claim of finding a mixed Nash Equilibrium **more than 100x faster than the state of the art** (under fair and comprehensive benchmarks).

 ![Comparison on six-player five-action GAMUT games](https://global.discourse-cdn.com/julialang/original/3X/d/8/d856babb6708c922af589f5e304d1c0818314ef3.png)

The iterative solver is based on the convergent theory of Turocy (2005), but optimized for speed and re-parametrized for better solution accuracy.

The algorithm is optimized for large dense games and solves a 4-player 66-action Blotto game (_same as considered by ADIDAS, 290 MB encoded as F32_) in around 12 seconds (_on a ThinkPad Gen 2_) to a relative maximal deviation incentive of less than 10^{-6}.

If you don’t believe me (_and I hope You don’t_), checkout the [detailed benchmarks](https://github.com/votroto/LogitNash.jl/wiki/Benchmarks) or try it yourself using [the published scripts](https://github.com/votroto/LogitNash.jl/tree/main/benchmark) for Bash and SLURM.

If you have suggestions for improvements or comparisons you would like to see, please get in touch.

P.S. If any of You experienced solver maintainers have tips and or lessons-learned for the final stable API, I would love to hear from you! 🙂  
P.P.S. This is my first package, sorry if this isn’t how packages are meant to be announced.