| pulp | HiGHS | |
|---|---|---|
| 19 | 3 | |
| 1,955 | 803 | |
| 1.7% | 3.6% | |
| 7.0 | 9.8 | |
| 2 days ago | 4 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pulp
- Algorithms - Researchers Approach New Speed Limit for Seminal Problem
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pulp VS timefold-solver - a user suggested alternative
2 projects | 4 Jan 2024
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References
Optimization with PuLP
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[2022 Day #19] I think I'll just lol-nope out of it
Now I have used Pulp library, which I guess does the heavy lifting.
- Python OR packages
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Operations research packages
Pyomo, it even has its own book. Additionally, CVXOPT focuses on convex optimization, PuLP on linear programming (it has lots of interfaces for other solvers).
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Successful Compiling COIN-OR CBC solver with Python Pulp ARM64
After installing https://github.com/coin-or/pulp via pip I failed to use the built-in cbc solver. Pulp comes with prebuilt binaries for Linux x32 & x64 but not for ARM, so it threw error while solving problem.
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Finding optimal inventory levels for clothing retailer.
Python has few libraries, one that I used it is PULP but there are others and as mentioned above, MS Excel has the Solver plugin to do it. Hope is hepful.
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Optimal number of muffins to produce
Hi, you should use an optimization linear programming package, also MS excel. With solver, if you use Python try to read the Documentaton of PULP library here is the link PULP Library Python hope it helps
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flopt: powerful optimization modeling tool
There are some optimization modeling tools, Pulp andScipy are known for linear programming (LP) modeling, CVXOPT and Pyomo for quadratic programming (QP).
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?
pyomo - An object-oriented algebraic modeling language in Python for structured optimization problems.
or-tools - Google's Operations Research tools:
PySCIPOpt - Python interface for the SCIP Optimization Suite
OptaPlanner - Java Constraint Solver to solve vehicle routing, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.
Bonmin - Basic Open-source Nonlinear Mixed INteger programming
osqp - The Operator Splitting QP Solver
Zeratool - Automatic Exploit Generation (AEG) and remote flag capture for exploitable CTF problems
csips - A pure-python integer programming solver
manim-rubikscube - A Manim implementation of the classic Rubik's Cube
exact
pulp_ansible - A Pulp plugin that manages Ansible content, i.e. roles, collections
clpz - Constraint Logic Programming over Integers