[JMLR (CCF-A)] PyPop7: A Pure-PYthon LibrarY for POPulation-based Black-Box Optimization (BBO), especially *Large-Scale* algorithm variants (from evolutionary computation, swarm intelligence, statistics, operations research, machine learning, mathematical optimization, meta-heuristics, auto-control etc.). [https://jmlr.org/papers/v25/23-0386.html]

Solver for Large-Scale Rank-One Semidefinite Relaxations

A ray-based library of Distributed POPulation-based OPtimization for Large-Scale Black-Box Optimization.

Code and computational experiments of the paper "Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs" by Cristian Ramírez-Pico, Ivana Ljubić and Eduardo Moreno. arXiv:2203.00752

Compressed Radiation Treatment Planning [NeurIPS'24, MP'2025, PMB'23]

Rapid Optimization Library

Build content-based image retrieval system using deep learning, applied some large scale similarity search technicals like Kdtree, LSH, Faiss.

Network-wide estimation of traffic flow and travel time with data-driven macroscopic models

Distributed Low-Memory Matrix Adaptation (D-LM-MA) Evolution Strategy.

Operations research projects will be added in this repo

A pure-MATLAB library of EVolutionary (population-based) OPTimization for Large-Scale black-box continuous Optimization (evopt-lso).

A pure-MATLAB library for POPulation-based Large-Scale Black-Box Optimization (pop-lsbbo).

This repository provides practical implementations, examples, and insights into various optimization methods, making it easier to understand and apply these concepts.

A deep reinforcement learning system for optimizing bridge maintenance decisions across municipal infrastructure fleets, implementing cross-subsidy budget sharing and cooperative multi-agent learning.

Deep Q-Network (DQN) implementation for optimal maintenance planning of 100-bridge fleet infrastructure using advanced reinforcement learning techniques and vectorized parallel training.

Deep Q-Network implementation for optimal bridge maintenance planning using Markov Decision Process formulation with vectorized parallel training. Based on Phase 3 (Vectorized DQN) from dql-maintenance-faster project.

Python implementation of a Genetic Algorithm to solve large scale binary knapsack problem

A flexible framework for SD-like algorithms in Julia.

C51 Distributional DQN (v0.8) for bridge fleet maintenance optimization. Implements categorical return distributions (Bellemare et al., PMLR 2017) with 300x speedup via vectorized projection. Combines Noisy Networks, Dueling DQN, Double DQN, PER, and n-step learning. Validated on 200-bridge fleet: +3,173 reward in 83 min (25k episodes).

Problem for the CEC 2013 Special Session on Large-Scale Real-Parameter Optimization