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/compass
공식 코드Combinatorial Optimization with Policy Adaptation using Latent Space Search
https://github.com/instadeepai/memento
공식 코드Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization
https://github.com/intellabs/floorset
공식 코드FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs
https://github.com/ita9naiwa/rlrd-aaai2022
공식 코드An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation
https://github.com/javilopiq/constraintnet.jl
공식 코드Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints
https://github.com/jayman91/ltr-predopt
공식 코드Decision-Focused Learning: Through the Lens of Learning to Rank
https://github.com/jesserem/Offline-LD
공식 코드Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
https://github.com/jhl-hust/vsr-lkh-v2
공식 코드Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems
https://github.com/jiaxianghuang/seqmo
공식 코드Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence Model
https://github.com/jinghanY/geoPrivacyAlbum
공식 코드https://github.com/jkuck/bpnn
공식 코드https://github.com/jokofa/neurols
공식 코드Learning to Control Local Search for Combinatorial Optimization
https://github.com/jokofa/nrr
공식 코드Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing Problems
https://github.com/jolibrain/wheatley
공식 코드https://github.com/jonatasbcchagas/aco_thop
공식 코드https://github.com/joonahn/BrECS
공식 코드Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction
https://github.com/kaist-silab/devformer
공식 코드DevFormer: A Symmetric Transformer for Context-Aware Device Placement
https://github.com/kaist-silab/dppbench
공식 코드DevFormer: A Symmetric Transformer for Context-Aware Device Placement
https://github.com/kaist-silab/meta-sage
공식 코드https://github.com/kaist-silab/symmetric_replay
공식 코드https://github.com/kaiwenw/JoinGym
공식 코드JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
https://github.com/keyonvafa/sequential-rationales
공식 코드https://github.com/khalil-research/walkability
공식 코드Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto
https://github.com/kurorororo/didp-models
공식 코드https://github.com/kyo-takano/EfficientCube
공식 코드https://github.com/lamda-bbo/neural-solver-selection
공식 코드https://github.com/ledenmat/pcqo-mis-benchmark
공식 코드Differentiable Quadratic Optimization For The Maximum Independent Set Problem
https://github.com/lianshumin576/mloandmcts
공식 코드Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
https://github.com/lirandepira/paoa-simulations
공식 코드Sub-universal variational circuits for combinatorial optimization problems
https://github.com/liuff19/casper
공식 코드https://github.com/liuyejia/gps_cl
공식 코드Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning
https://github.com/logo-cuhksz/rethink_mcts_tsp
공식 코드Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP Solvers
https://github.com/lorenz92/qal-bp
공식 코드QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing
https://github.com/lpmp/bdd
공식 코드DOGE-Train: Discrete Optimization on GPU with End-to-end Training
https://github.com/luckyseasalt/pioneer
공식 코드An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.