| Name | Version | Summary | date |
| swiglpk |
5.0.12 |
swiglpk - Simple swig bindings for the GNU Linear Programming Kit |
2024-11-25 14:04:41 |
| mozjpeg-lossless-optimization |
1.1.5 |
Optimize JPEGs losslessly using MozJPEG |
2024-11-22 14:54:14 |
| qpalm |
1.2.5 |
Proximal Augmented Lagrangian method for Quadratic Programs |
2024-11-22 00:11:35 |
| flowty |
2.1.0 |
Flowty Network Optimization Solver |
2024-11-21 10:59:30 |
| gurobi-modelanalyzer |
2.1.0 |
Model analysis tools for explaining ill-conditioning and analyzing solutions. |
2024-11-12 13:27:33 |
| fcmaes |
1.6.11 |
A Python 3 gradient-free optimization library. |
2024-11-07 11:02:08 |
| SplitFXM |
0.4.6 |
1D Finite-Difference/Volume Split Newton Solver |
2024-10-24 17:52:48 |
| xpressinsight |
1.12.0 |
FICO Xpress - Insight Python package |
2024-10-22 10:54:11 |
| pulp-utils |
0.1.7 |
pulp_utils is a library with utility tools for PuLP |
2024-10-14 06:59:42 |
| optimas |
0.7.1 |
Optimization at scale, powered by libEnsemble |
2024-09-20 21:16:10 |
| qpax |
0.0.9 |
Differentiable QP solver in JAX. |
2024-09-16 17:04:00 |
| pysors |
1.0.0 |
Fork of second-order-random-search with scipy.minimize-like interface. |
2024-09-14 08:35:26 |
| findi-descent |
0.2.0 |
FinDi: Finite Difference Gradient Descent can optimize any function, including the ones without analytic form, by employing finite difference numerical differentiation within a gradient descent algorithm. |
2024-09-14 03:48:51 |
| eesrep |
0.1.5 |
EESREP is a component based energy system optimisation python module. |
2024-09-03 08:36:16 |
| cma |
4.0.0 |
CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python |
2024-09-03 07:46:12 |
| stelladb |
0.2.13 |
Includes functions to upload DESC and VMEC data to the stellarator database. |
2024-08-28 04:57:54 |
| fortoptim |
0.0.10 |
Another optimization package |
2024-08-24 14:29:23 |
| FortOptim |
0.0.4 |
My custom optimization package |
2024-08-24 08:09:14 |
| pyhms |
0.1.1 |
The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple python implementation with examples of using different population engines. |
2024-08-22 15:44:21 |
| obsidian-apo |
0.8.3 |
Automated experiment design and black-box optimization |
2024-08-22 02:33:14 |