osqp
TexasSolver
osqp | TexasSolver | |
---|---|---|
4 | 44 | |
1,565 | 1,556 | |
1.6% | - | |
8.1 | 4.0 | |
8 days ago | 7 days ago | |
C | C++ | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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osqp
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Best/Any Convex Optimization Solver for Rust?
There's also two bindings for the osqp library (which is written in C), osqp published 2 years ago and osqp-rust published 3 months ago. I don't know what are the differences between them, but they both target osqp 0.6.2 (released in 2021) while the last released version is osqp 0.6.3 which was released last week.
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Cvxpy probs
Cvxpy is overkill for a standard quadratic program. I’d recommend trying OSQP https://osqp.org which can take advantage of sparsity.
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Ask HN: Do you use an optimization solver? Which one? Why? Do you like it?
I have been using OSQP [1] quite a bit in a project where I needed to solve many quadratic programs (QPs). When I started the project, OSQP didn't exist yet; I ended up using both cvxopt and MOSEK; both were frustratingly slow.
After I picked up the project again a year later, I stumbled across the then new OSQP. OSQP blew both cvxopt and MOSEK out of the water (up to 10 times faster) in terms of speed and quality of the solutions. Plus the C interface was quite easy to use and super easy (as far as numerics C code goes) to integrate into my larger project.
[1] https://osqp.org/
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What's the industry standard "fast" library for optimization methods?
For quadratic programming—which is a class of problems in convex optimization, which is a sub-field of numerical optimization in general—a solver that is frequently used is OSQP. Although it is implemented in C++ you can also use it in Python thanks to its bindings. If your goal is to use a solver that's state-of-the-art and relatively versatile it is a good pick. If your goal is to find the best solver for a given problem, then there is no one-stop-shop. For example in this benchmark OSQP was the best-performing solver for sparse problems but quadprog performed better on dense problems.
TexasSolver
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The Law of Large Numbers, or Why It Is a Bad Idea to Go to the Casino
I use GTO+ which is proprietary.
I just tried this one which works like a charm (just run the exe from the zip in github releases ; even comes pre-loaded with a wide amount of preflop ranges, which seem to come from a previous solve) : https://github.com/bupticybee/TexasSolver
Searching with "poker solver haskell" only seem to show very immature projects.
- Regarding bupticybee's TexasSolver on github, are there any additional resources that could assist with maneuvering/understanding the application?
- Any free GTO Solvers?
- Poker solver for Mac?
- Poker (NLH) model?
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Poker solvers for dummies: part 3 - the best solvers for beginners!
Texas Solver is a free one. I now of atleast one other free one that also works in the brower ( though with reduced RAM), but sadly forgot its name; if I remember I'll edit.
- Free postflop 100BB Cash Solvers
- Free solver with solid interface
- Is Piosolver worth it?
- Advanced Poker Math Book Suggestions
What are some alternatives?
MControlCenter - An application that allows you to change the settings of MSI laptops running Linux
TexasHoldemSolverJava - A Java implemented Texas holdem and short deck Solver
HiGHS - Linear optimization software
holdem - A Lightweight Texas Hold'em Poker Game
quadprog - Quadratic Programming Solver
PokerHandEvaluator - Poker-Hand-Evaluator: An efficient poker hand evaluation algorithm and its implementation, supporting 7-card poker and Omaha poker evaluation
golomb-solver - Create Golomb rulers with constraint programming
hyStrath - Hypersonic / Rarefied gas dynamics code developments (GPL-3.0)
vroom - Vehicle Routing Open-source Optimization Machine
desktop-postflop - [Development suspended] Advanced open-source Texas Hold'em GTO solver with optimized performance
python-mip - Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs
awesome-cpp - A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.