osqp | HiGHS | |
---|---|---|
4 | 3 | |
1,565 | 800 | |
1.6% | 3.3% | |
8.1 | 9.8 | |
8 days ago | 9 days ago | |
C | C++ | |
Apache License 2.0 | MIT License |
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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.
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?
MControlCenter - An application that allows you to change the settings of MSI laptops running Linux
or-tools - Google's Operations Research tools:
quadprog - Quadratic Programming Solver
OptaPlanner - Java Constraint Solver to solve vehicle routing, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.
golomb-solver - Create Golomb rulers with constraint programming
csips - A pure-python integer programming solver
vroom - Vehicle Routing Open-source Optimization Machine
exact
python-mip - Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs
clpz - Constraint Logic Programming over Integers