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Search results for python constrained optimization

constrained-optimization x

25 search results found

Gradient Free Optimizers ⭐ 1,072

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Neuromancer ⭐ 621

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.

A general-purpose, deep learning-first library for constrained optimization in Pytorch

Powell's Derivative-Free Optimization solvers.

Open Box ⭐ 64

Generalized and Efficient Blackbox Optimization System.

Library for Multi-objective optimization in Gradient Boosted Trees

A Python Library for modeling combinatorial constrained problems

Landscapes ⭐ 50

A dependency free library of standardized optimization test functions written in pure Python.

An interior-point method written in python for solving constrained and unconstrained nonlinear optimization problems.

A SciPy compatible super fast Python implementation for Particle Swarm Optimization.

Python constraint programming library

Safe Pdp ⭐ 19

Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.

Pytorch Lattice ⭐ 18

A PyTorch implementation of constrained optimization and modeling techniques

Python library to implement advanced trading strategies using machine learning and perform backtesting.

LAMBDA is a model-based reinforcement learning agent that uses Bayesian world models for safe policy optimization

Python implementation of the SQP-GS algorithm for nonsmooth, nonconvex constrained optimization

The Modified Differential Multiplier Method (MDMM) for PyTorch

Stochasticfrankwolfe ⭐ 9

Implementation of the Stochastic Frank Wolfe algorithm in TensorFlow and Pytorch.

Constrnmpy ⭐ 8

A Python package for constrained Nelder-Mead optimization.

Optimized and benchmarked parallel Genetic Algorithm with inequality constraints, and a scipy-like interface

Ags_nlp_solver ⭐ 7

Derivative-free nonlinear global optimizer with python interface

Optimizers for/and sklearn compatible Machine Learning models

Mep Orthogonal Nmf ⭐ 6

Clustering and resource allocation using Deterministic Annealing Approach and Orthogonal Non-negative Matrix Factorization O-(NMF)

Pytorch Cpo ⭐ 5

PyTorch implementation of Constrained Policy Optimization

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