https://github.com/cheikh025/DistrictNet
공식 코드DistrictNet: Decision-aware learning for geographical districting
Papers with Code 아카이브
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/chendiqian/IPM_MPNN
공식 코드Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
https://github.com/chenzhaiyu/points2poly
공식 코드Reconstructing Compact Building Models from Point Clouds Using Deep Implicit Fields
https://github.com/childtang/prance
공식 코드PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference
https://github.com/chkwon/TSPDrone-RL
공식 코드A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
https://github.com/ciam-group/drhg
공식 코드https://github.com/ciam-group/icam
공식 코드Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
https://github.com/ciam-group/nco_code
공식 코드Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
https://github.com/cicirello/permutation-crossover-landscape-analysis
공식 코드A Survey and Analysis of Evolutionary Operators for Permutations
https://github.com/cimy-wang/fusdreamer
공식 코드FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification
https://github.com/closest-git/gss
공식 코드Learning the Markov Decision Process in the Sparse Gaussian Elimination
https://github.com/corail-research/SeaPearl.jl
공식 코드SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning
https://github.com/corentindelacour/Lagrange-oscillatory-neural-network
공식 코드Lagrange Oscillatory Neural Networks for Constraint Satisfaction and Optimization
https://github.com/corentindelacour/self-adaptive-im
공식 코드https://github.com/cornell-zhang/heurigym
공식 코드HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization
https://github.com/crazydonkey200/neural-symbolic-machines
공식 코드Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
https://github.com/cschen1205/lmea
공식 코드https://github.com/dan-lb/quantum_kernel_discovery_ad_hep
공식 코드https://github.com/danifuertes/top_transformer
공식 코드TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem
https://github.com/datphamvn/hsevo
공식 코드https://github.com/davzha/recurrently_predicting_hypergraphs
공식 코드Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets
https://github.com/dawoz/jsp-deeprl-seq2seq
공식 코드Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach
https://github.com/ddrakulic/bqnco
공식 코드BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
https://github.com/deepmind/neural_lns
공식 코드Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
https://github.com/dimesteam/dimes
공식 코드DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems
https://github.com/dmitrysorokin/treedqn
공식 코드https://github.com/domain-independent-dp/didp-rs
공식 코드Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial Optimization
https://github.com/dongdongbh/RCQL
공식 코드One model Packs Thousands of Items with Recurrent Conditional Query Learning
https://github.com/ds4dm/ecole
공식 코드Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
https://github.com/ds4dm/ecole-paper
공식 코드https://github.com/ds4dm/learn2branch
공식 코드Exact Combinatorial Optimization with Graph Convolutional Neural Networks
https://github.com/ds4dm/ml4co-competition
공식 코드The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
https://github.com/duncanmcelfresh/RobustKidneyExchange
공식 코드https://github.com/dyc941126/gat-pcm
공식 코드Pretrained Cost Model for Distributed Constraint Optimization Problems
https://github.com/eddylxf23/ccmckp
공식 코드Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
https://github.com/eddylxf23/ddccmckp
공식 코드Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.