35 skills found · Page 1 of 2
mit-acl / Clippergraph-theoretic framework for robust pairwise data association
StephLin / KCPK-Closest Points and Maximum Clique Pruning for Efficient and Effective 3-D Laser Scan Matching (RA-L 2022)
ryanrossi / PmcParallel Maximum Clique Library
Thinklab-SJTU / ML4CO Bench 101ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs.
xuzijian629 / Combopt ZeroA reinforcement learning based solver for combinatorial problems
donfaq / Max CliqueImplementation of branch and bound algorithm for maximum clique problem
LijunChang / MC BRBMaximum clique computation over large sparse graphs
shah314 / CliqueGenetic Algorithm for the Maximum Clique Problem
jamestrimble / Max Weight Clique InstancesBenchmark instances for the maximum weight clique problem
jwalteros / DOmegaLibrary for finding maximum cliques on graphs
psanse / CliSATAn exact algorithm for the maximum clique problem (MCP) which improves over state-of-the-art approaches in some cases by orders of magnitude
darrenstrash / Open McsAn open implementation of the MC family of maximum clique algorithms
linhongseba / MaximumCliqueThe implementation for maximum clique enumeration algorithm
ahgamut / CliquematchFinding correspondence via maximum cliques
lanl / Parallel Quantum AnnealingParallel Quantum Annealing
donfaq / LP Max CliqueImplementation of branch and bound algorithm for maximum clique problem with cplex
fontanf / StablesolverA solver for the maximum(-weight) independent set and the maximum(-weight) clique problems
abhik1505040 / Max Clique ImplementationsDifferent algorithms to find maximum clique in a graph.
kshitija2 / Maximum Weight Clique Problem SolverImplemented two algorithms from the paper “Two Efficient Local Search Algorithms for Maximum Weight Clique Problem” by Yiyuan Wang, Shaowei Cai and Minghao Yin. This paper introduces two heuristics and develops two local search algorithms for Maximum Weight Clique problem (MWCP). MWCP is an important generalization of the Maximum Clique problem (MCP) with wide applications. First heuristic is strong configuration checking (SCC) for reducing cycling in local search which results in LSCC algorithm. Moreover, to improve the performance on massive graphs, there is second heuristic called Best from Multiple Selection (BMS) to select the swapping vertex pair quickly and effectively. The BMS heuristic is used to improve LSCC, resulting in the LSCC+BMS algorithm. Programming Language: Python
tentone / Max CliqueGraph theory experiments for the maximum clique problem using python.
