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.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/paulorocosta/ai-for-tsp-competition
공식 코드The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
https://github.com/pawelswoboda/rama
공식 코드https://github.com/pengguo318/RoutingProblemGANN
공식 코드Solve routing problems with a residual edge-graph attention neural network
https://github.com/penlu/bespoke-gnn4do
공식 코드https://github.com/pi-star-lab/unscalable-heuristic-approximator
공식 코드The (Un)Scalability of Heuristic Approximators for NP-Hard Search Problems
https://github.com/pkutan/sawt
공식 코드Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem
https://github.com/pmohanty98/edlp
공식 코드https://github.com/pqros/fastcover
공식 코드https://github.com/prosysscience/JSS
공식 코드A Reinforcement Learning Environment For Job-Shop Scheduling
https://github.com/prosysscience/JSSEnv
공식 코드A Reinforcement Learning Environment For Job-Shop Scheduling
https://github.com/prosysscience/job-shop-scheduling
공식 코드Decomposition Strategies and Multi-shot ASP Solving for Job-shop Scheduling
https://github.com/pytorch/rl
공식 코드RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
https://github.com/qcappart/ROD_oracle
공식 코드https://github.com/qcappart/hybrid-cp-rl-solver
공식 코드Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
https://github.com/qcappart/learning-DD
공식 코드https://github.com/qchen95/Coupled-input-output-DR
공식 코드https://github.com/qiang-ma/graph-pointer-network
공식 코드Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
https://github.com/realityengines/local_search
공식 코드https://github.com/reso1/mcfs
공식 코드https://github.com/richoux/LearningCostFunctions
공식 코드Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling
https://github.com/richoux/microrts-uncertainty
공식 코드https://github.com/richoux/pobo
공식 코드Injecting Combinatorial Optimization into MCTS: Application to the Board Game boop
https://github.com/richoux/taunt
공식 코드Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
https://github.com/ricvolpi/domain-shift-robustness
공식 코드Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets
https://github.com/rleap-project/d2l
공식 코드Learning General Policies from Small Examples Without Supervision
https://github.com/rnn-vca-co/rnn-vca-co
공식 코드Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization Problems
https://github.com/robbertreijnen/dr-alns
공식 코드Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
https://github.com/robbertreijnen/gs-modac
공식 코드Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
https://github.com/robo-sq/dash
공식 코드Fast Parallel Algorithms for Statistical Subset Selection Problems
https://github.com/roinaveiro/sbbo
공식 코드https://github.com/romeov/ml4co-competition
공식 코드Instance-wise algorithm configuration with graph neural networks
https://github.com/royalskye/agh
공식 코드Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling
https://github.com/royalskye/omni-vrp
공식 코드Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
https://github.com/royalskye/routing-mvmoe
공식 코드MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
https://github.com/rsilva8/MISA
공식 코드Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.