OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.

A java implementation of the famous Lin-Kernighan heuristics algorithm implemented for graphic (symmetric) TSP

Approximation Algorithm for the NP-Complete problem of finding a vertex cover of minimum weight in a graph with weighted vertices. Guarantees an answers at most 2 times the optimal minimum weighted vertex cover

Explore different algorithms for Maximum 0-1 Knapsack

演算法筆記

Car Sequencing Problem solved by constraint programming approach and Choco Solver.

Google Hash Code 2018 Online Qualification Round

A Certifier algorithm to check a particular solution to the NP-Complete 3-Sat problem

Collection of Assignments and Programs For CS 146: Data Structures and Algorithms

Algorithmic Code Snippets

analyzer of selected task scheduling heuristics.

Randomized Solver for the Non-Betweenness Problem

Java & Python Implementation of the Boolean Satisfiability Problem Solver

This research was supported by the Ministry of Science and Technology in Taiwan under the grants MOST 107-2813-C-845-025-E.

A fast heuristic algorithm for solving high-density variants of the subset-sum problem

an application aimed to teach dedicated learners of NP related algorithms

This project was to implement a solution to the NP-Complete Vertex Cover problem: finding the minimum set of vertices required to form a cover of a given graph. A cover fits a graph when all vertices in the graph have at least 1 link to a vertex contained within the cover set.