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.

finquant

A program for financial portfolio management, analysis and optimisation.

dlux

Differentiable Physical Optics Propagation in Jax

graphilp

A Python API to automatically cast graph-related optimisation problems into ILP instances for Gurobi

lab-camera-optimizer

Automated camera placement optimiser for markerless biomechanics motion capture labs

cobyqa

A derivative-free solver for general nonlinear optimization.

district-energy-model

Python-based district energy system model for simulation and optimisation of heat, electricity, and storage at district scale.

calliope

A multi-scale energy systems modelling framework

myspokenlanguagedetection

Spoken language identification with CNN and RNN - Early Stage

macop

Minimalist And Customisable Optimisation Package

calisim

A toolbox for the calibration and evaluation of simulation models.

pykp

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

tide-feature-selection

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

insurance-optimise

Constrained portfolio rate optimisation for UK personal lines insurance, with FCA ENBP enforcement, demand-linked objectives, efficient frontier, 3-objective Pareto surface with fairness, model quality LR adjustment, robust multi-line reinsurance, linear risk sharing pools, and convex De Finetti reinsurance design

compas-shapeop

COMPAS binding for the ShapeOp library using nanobind

xad

High-Performance Automatic Differentiation for Python

edo

Generating artificial datasets through evolution.