Combinatorial Optimization
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Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zz1358m/mcts-ahd-master
공식 코드Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design
https://gitlab.com/verel/qap-sat
공식 코드Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances
https://github.com/8023looker/med-rr
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/8023looker/versatune
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/AashrayAnand/rubiks-cube-reinforcement-learning
https://github.com/Brendan-Reid1991/CFD-Algorithms
Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis
https://github.com/Dalkio/RL_rubiks
https://github.com/DavidLee-4032/Graph_Opt
https://github.com/DeepLearningCUCS/RLVRP
Reinforcement Learning for Solving the Vehicle Routing Problem
https://github.com/Ericolony/QAOA
https://github.com/FeiLiu36/LLM4MOEA
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
https://github.com/GeminiLight/virne
https://github.com/Guillem96/pointer-nn-pytorch
https://github.com/Howardhuang98/Pointer_network
https://github.com/IvanIsCoding/GNN-for-Combinatorial-Optimization
Combinatorial Optimization with Physics-Inspired Graph Neural Networks
https://github.com/JasperBusschers/multi-objective-Rubik-s-cube
https://github.com/JoseVillagranE/Pointer-Networks
https://github.com/KeatonMueller/rl-cube
https://github.com/KolodziejczykWaldemar/universal-ptr-network
https://github.com/LoganZhao1997/last_mile_challenge
https://github.com/Loriba4/Lori-Baxter
https://github.com/MK-tech20/MDRRT-scheduling-dwave
https://github.com/MindSpore-scientific-2/code-3/tree/main/The_RefinedWeb_Dataset_for_Falcon
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
https://github.com/NTT123/pointer-networks
https://github.com/Nina-Konovalova/TSP-RL-Skoltech_project
https://github.com/OptMLGroup/VRP-RL
Reinforcement Learning for Solving the Vehicle Routing Problem
https://github.com/Rintarooo/TSP_DRL_PointerNet
https://github.com/Rintarooo/TSP_DRL_PtrNet
https://github.com/Rintarooo/TSP_RL_Pointer_Actor_Critic
https://github.com/Rintarooo/VRP_DRL_MHA
https://github.com/Rintarooo/VRP_MHA
https://github.com/Roberto09/Dynamic-Attention-Model-for-VRP---Pytorch
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
https://github.com/Thinklab-SJTU/One-Shot-Cardinality-NN-Solver
https://github.com/VasaKiDD/alpha-regex
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
https://github.com/XAI-liacs/BLADE
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.