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/instadeepai/poppy
Efficient Active Search for Combinatorial Optimization Problems
https://github.com/isotlaboratory/ml4vrp
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
https://github.com/jiangluyu1998/decils-pbo
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean Optimization
https://github.com/kaletap/deep-cube-rl
https://github.com/keyonvafa/career-code
https://github.com/khalil-research/pyepo
Contrastive Losses and Solution Caching for Predict-and-Optimize
https://github.com/lairning/vrp-drl
https://github.com/longkangli/pfss-il
Neural Combinatorial Optimization with Reinforcement Learning
https://github.com/louisv123/COLGE
https://github.com/martius-lab/blackbox-backprop
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
https://github.com/microsoft/maro
https://github.com/mits58/Pointer-Networks
https://github.com/mkovalski/cs4995_cube
https://github.com/mustelideos/optw_rl
A Reinforcement Learning Approach to the Orienteering Problem with Time Windows
https://github.com/mustelideos/td-opswtw-competition-rl
A Reinforcement Learning Approach to the Orienteering Problem with Time Windows
https://github.com/nascarsayan/dl-rubiks-autodidactic-solver
https://github.com/nathangrinsztajn/rubiks_cube
https://github.com/naver/bq-nco
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
https://github.com/niansong1996/wassp
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
https://github.com/nico-koltermann/contextual-evolution-of-heuristics
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
https://github.com/ortec/euro-neurips-vrp-2022-quickstart
https://github.com/pcyin/pytorch_neural_symbolic_machines
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
https://github.com/pemami4911/neural-combinatorial-rl-pytorch
Neural Combinatorial Optimization with Reinforcement Learning
https://github.com/q3erf/topo_bbgm
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
https://github.com/raphaelavalos/attention_tsp_graph_net
https://github.com/ravi-lanka-4/CoPiEr
https://github.com/ricvolpi/generalize-unseen-domains
Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets
https://github.com/rigetti/gym-forest
Automated quantum programming via reinforcement learning for combinatorial optimization
https://github.com/rishiyama/Test-Time-Augmentation-for-Traveling-Salesperson-Problem
https://github.com/rizwan09/paper
https://github.com/robbiejones96/RubiksSolver
https://github.com/sadhikamalladi/RL-Coloring
https://github.com/sblayush/Tensorflow-Attention
https://github.com/sblayush/summarization
https://github.com/sclbd/accelerated-lpbox-admm
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.