https://bitbucket.org/lbliek2/idone
공식 코드Black-box Combinatorial Optimization using Models with Integer-valued Minima
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.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/1041877801/COMBHelper
공식 코드COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems
https://github.com/1716757342/mmsr
공식 코드MMSR: Symbolic Regression is a Multi-Modal Information Fusion Task
https://github.com/1918190/copf
공식 코드Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation
https://github.com/8023looker/datasculpt
공식 코드DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/AdelNabli/MCN
공식 코드Curriculum learning for multilevel budgeted combinatorial problems
https://github.com/Akimoto-Cris/RD_PRUNE
공식 코드Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
https://github.com/ArkadiyD/SAGOMEA
공식 코드A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
https://github.com/BeloborodovDS/SIMCIM-RL
공식 코드Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization
https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/UDC-Large-scale-CO-master
공식 코드https://github.com/ChristophHertrich/neural-knapsack
공식 코드Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size
https://github.com/CryoCardiogram/ijcai-cache-loss-pno
공식 코드Contrastive Losses and Solution Caching for Predict-and-Optimize
https://github.com/Dana-L/PSDMF
공식 코드https://github.com/Dtrycode/ToupleGDD
공식 코드ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning
https://github.com/EsiSeraj/FireCommander2020
공식 코드FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot Teams
https://github.com/EtorArza/SupplementaryKMMHamming
공식 코드Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
https://github.com/FWen/mcme
공식 코드https://github.com/GTorlai/PastaQ.jl
공식 코드Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
https://github.com/Giventicket/BlackoutDIFUSCO
공식 코드https://github.com/Haipeng-Chen/RL4IM-Contingency
공식 코드Contingency-Aware Influence Maximization: A Reinforcement Learning Approach
https://github.com/Hanjun-Dai/graph_comb_opt
공식 코드https://github.com/HumainLab/EquivaMap
공식 코드EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations
https://github.com/JHL-HUST/VSR-LKH
공식 코드https://github.com/JasonZhujp/RELIEF
공식 코드RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
https://github.com/JayMan91/aaai_predit_then_optimize
공식 코드Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
https://github.com/Joey-Shen/pb-dfs
공식 코드https://github.com/JorikJooken/MCTS01Knapsack
공식 코드https://github.com/JorikJooken/MCTSQuayCraneSchedulingNonCrossingConstraints
공식 코드https://github.com/K-Coconut/MCPBenchmark
공식 코드A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs
https://github.com/LIONS-EPFL/LearningToRemoveCutsILP
공식 코드https://github.com/LOGO-CUHKSZ/ASP
공식 코드https://github.com/LPMP/LPMP
공식 코드https://github.com/LeonResearch/GraphComBO
공식 코드Bayesian Optimization of Functions over Node Subsets in Graphs
https://github.com/LouisBouvier/WarcraftShortestPaths.jl
공식 코드Learning with Combinatorial Optimization Layers: a Probabilistic Approach
https://github.com/OPUSLab/XORSATwithpbits
공식 코드All-to-all reconfigurability with sparse and higher-order Ising machines
https://github.com/OptimalPursuit/navigating_uncertainty_in_mpp
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.