Powell's Derivative-Free Optimization solvers.
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Updated
May 24, 2024 - Fortran
Powell's Derivative-Free Optimization solvers.
A collection of Benchmark functions for numerical optimization problems
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.
The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software
Optimization functions for Julia
A comprehensive collection of optimization methods with focus on research.
Benchmarking optimization solvers.
A set of Jupyter notebooks that investigate and compare the performance of several numerical optimization techniques, both unconstrained (univariate search, Powell's method and Gradient Descent (fixed step and optimal step)) and constrained (Exterior Penalty method).
Course Projects for Operations Research in USTC (2021 Fall).
Solve a max-cut problem using a quantum computer
This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks(PINNs)"
Optimization algorithms by M.J.D. Powell
A Python package integrating around ten unconstrained optimization algorithms, inclusive of 2D/3D visualizations for comparative analysis, and incorporated matrix operations.
Perform basic image segmentation using discrete quadratic models (DQM) and hybrid solvers.
PRIMA: Reference Implementation for Powell's methods with Modernization and Amelioration
numerical optimization subroutines in Fortran generated by ChatGPT-4
LBFGS-Lite: A header-only L-BFGS unconstrained optimizer.
This repository contains assignments completed in the course "Convex Optimisation" using python
A repository of optimization algorithms implemented in Python & MATLAB for mathematical optimization problems. Algorithms such as Genetic Algorithm, PSO, linear programming, and etc.
Optimizers for/and sklearn compatible Machine Learning models
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