Optimisation Python Packages

optimistix

Nonlinear optimisation (root-finding, least squares, ...) in JAX+Equinox. https://docs.kidger.site/optimistix/

linopy

Linear optimization with N-D labeled arrays in Python

pypsa

PyPSA: Python for Power System Analysis

bluepyopt

Blue Brain Python Optimisation Library

bluepyemodel

Blue Brain Python Electrical Modeling Pipeline

pyvmcon

Python implementation of the VMCON non-linear constrained optimiser.

flixopt

Vector based Energy Optimization Framework

flatland-rl

The Flatland Framework is a multi-purpose environment to tackle problems around resilient resource allocation under uncertainty. It is designed to be a flexible and method agnostic to solve a wide range of problems in the field of operations research and reinforcement learning.

cobyqa

A derivative-free solver for general nonlinear optimization.

calliope

A multi-scale energy systems modelling framework

dlux

Differentiable Physical Optics Propagation in Jax

finquant

A program for financial portfolio management, analysis and optimisation.

pykp

Tooling for sampling and solving instances of the 0-1 Knapsack Problem

calisim

A toolbox for the calibration and evaluation of simulation models.

nmrpoise

A TopSpin-compatible NMR package for Parameter Optimisation by Iterative Spectral Evaluation

xad

High-Performance Automatic Differentiation for Python

macop

Minimalist And Customisable Optimisation python Package

insurance-optimise

Constrained portfolio rate optimisation for insurance pricing — SLSQP, FCA ENBP, efficient frontier, shadow prices, JSON audit trail

lab-camera-optimizer

Automated camera placement optimiser for markerless biomechanics motion capture labs

compas-shapeop

COMPAS binding for the ShapeOp library using nanobind

edo

A library for generating artificial datasets through genetic evolution.

piglot

A package for the optimisation of numerical responses

mooda-dna

A DNA design and assembly method based on multi-objective optimization.

tide-feature-selection

Feature selection for real ML pipelines: filters, wrappers, and metaheuristics with cross-validation, reproducibility, and bundled benchmarks.