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

Google Hash Code 2018 Online Qualification Round

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

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

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

analyzer of selected task scheduling heuristics.

Randomized Solver for the Non-Betweenness Problem

Algorithmic Code Snippets

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

Java & Python Implementation of the Boolean Satisfiability Problem Solver

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.

an application aimed to teach dedicated learners of NP related algorithms

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