Numerical Optimization Python Packages

mpmath

Python library for arbitrary-precision floating-point arithmetic

cvxpy

A Python-embedded modeling language for convex optimization problems.

qpsolvers

Quadratic programming solvers in Python with a unified API

moocore

Core Mathematical Functions for Multi-Objective Optimization

qdldl

Python interface to the QDLDL (https://github.com/osqp/qdldl) free LDL factorization routine for quasi-definite linear systems

pot

POT : Python Optimal Transport

daqp

A dual active-set algorithm for convex quadratic programming

cvxpy-base

A Python-embedded modeling language for convex optimization problems.

better-optimize

A friendlier front-end to scipy.optimize

unopy

With Uno, finally take full control of your SQP/barrier solver for nonlinearly constrained optimization

dfo-ls

Python-based Derivative-Free Optimizer for Least-Squares

tff-nightly

High-performance TensorFlow library for quantitative finance.

py-bobyqa

Python-based Derivative-Free Optimization with Bound Constraints

pints

Probabilistic Inference on Noisy Time Series

pdfo

Powell's Derivative-Free Optimization solvers.

pypop7

[JMLR (CCF-A)] PyPop7: A Pure-PYthon LibrarY for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* algorithm variants. In the near future, we will add Learning-Based Optimizers as its extensions. [https://jmlr.org/papers/v25/23-0386.html]

optimagic

optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.

pyglobalsearch

A multistart framework for global optimization with scatter search and local NLP solvers written in Rust

estimagic

optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.

galahad-optrove

A library of modern Fortran modules for nonlinear optimization

dfogn

DFO-GN: Derivative-Free Optimization using Gauss-Newton

tf-quant-finance

High-performance TensorFlow library for quantitative finance.

torchzero

Modular optimization library for PyTorch (work-in-progress).

tyba-cvxpy

A domain-specific language for modeling convex optimization problems in Python.