GPUDemos
HiGHS
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GPUDemos
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A Collection of Compute Shader GPU Simulations [Source Code Included]
All source code can be found here along with further explanation of this project: https://github.com/Wojimus/GPUDemos
HiGHS
- Algorithms - Researchers Approach New Speed Limit for Seminal Problem
- HiGHS: High performance open source MILP and QP solver
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Ask HN: Do you use an optimization solver? Which one? Why? Do you like it?
I've been using CBC via python-mip (https://github.com/coin-or/python-mip). It's great because it's got a super clean interface (milp variables/expressions/constraints), the code is quite accessible, and it's low overhead which makes it good for solving many very small problems.
Community sentiment seems to be beginning to shift toward favouring the HiGHS solver (https://github.com/ERGO-Code/HiGHS) over CBC. Something I'm keeping a close eye on.
nextmv seems to pitch itself as a generic solving ("decision automation") platform or something (unclear). But it seems that the only fleshed out product offering is for vehicle routing, based on the docs. Are there plans to offer, for instance, a solver binary that can be used to solve generic problems?
Also all the github repos under https://github.com/nextmv-io are private, so links from docs are 404.
What are some alternatives?
or-tools - Google's Operations Research tools:
OptaPlanner - Java Constraint Solver to solve vehicle routing, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.
osqp - The Operator Splitting QP Solver
csips - A pure-python integer programming solver
exact
clpz - Constraint Logic Programming over Integers
python-mip - Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs
SciPy - SciPy library main repository
meal-scheduler
optaplanner-quickstarts - Mirror of https://github.com/apache/incubator-kie-optaplanner-quickstarts
golomb-solver - Create Golomb rulers with constraint programming