golomb-solver
osqp
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golomb-solver | osqp | |
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1 | 4 | |
5 | 1,565 | |
- | 2.9% | |
0.0 | 8.1 | |
10 months ago | 5 days ago | |
Scala | C | |
- | Apache License 2.0 |
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golomb-solver
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Ask HN: Do you use an optimization solver? Which one? Why? Do you like it?
CPLEX (by IBM). The documentation can be a bit thin sometimes. But its fast. Most benchmarks place it ahead of the google cloud products.
For fun I made this Golomb ruler solver using cplex: https://github.com/strateos/golomb-solver
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.
What are some alternatives?
HiGHS - Linear optimization software
python-mip - Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs
MControlCenter - An application that allows you to change the settings of MSI laptops running Linux
optaplanner-quickstarts - Mirror of https://github.com/apache/incubator-kie-optaplanner-quickstarts
quadprog - Quadratic Programming Solver
csips - A pure-python integer programming solver
vroom - Vehicle Routing Open-source Optimization Machine
clpz - Constraint Logic Programming over Integers
meal-scheduler