Knowledge-Driven Large-Scale Multi-Objective Evolutionary Learning for Interval Prediction of Key Quality Indicators in Blast Furnace Ironmaking Process

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

A flexible framework for SD-like algorithms in Julia.

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

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).

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.

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

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

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.

Operations research projects will be added in this repo

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

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

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

A First-Order LP Solver Accelerated on Multiple GPUs

A GPU-Accelerated First Order Solver for Convex Quadratic Programming

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

The Conjugate Gradient (CG) method is an efficient iterative algorithm for solving large, sparse systems of linear equations where the matrix is symmetric and positive-definite. It finds the minimum of a quadratic function by generating conjugate search directions, ensuring convergence in at most steps for an matrix.Solver

LSQR is an iterative method for solving large, sparse, linear systems of equations and linear least-squares problems, including under- or over-determined and rank-deficient systems. It uses the Lanczos bidiagonalization process to provide a robust alternative to conjugate gradients, offering better numerical stability. Solver