https://github.com/victordarvariu/solving-graph-pgg
공식 코드Solving Graph-based Public Good Games with Tree Search and Imitation Learning
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/vidalt/HGS-CVRP
공식 코드Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood
https://github.com/viktour19/culingam
공식 코드AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
https://github.com/water-mirror/DPR
공식 코드Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search
https://github.com/water-mirror/NeuLNS
공식 코드https://github.com/wenkelf/copt
공식 코드Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
https://github.com/wohnjayne/eda-suite
공식 코드Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial Optimization
https://github.com/wondergo2017/tsp-hac
공식 코드Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum
https://github.com/wouterkool/attention-learn-to-route
공식 코드https://github.com/wouterkool/attention-tsp
공식 코드https://github.com/wroth8/bnc
공식 코드Differentiable TAN Structure Learning for Bayesian Network Classifiers
https://github.com/wuuu110/Hercules
공식 코드https://github.com/wuuu110/NCO-solvers-for-VRP
공식 코드https://github.com/xi-l/pmoco
공식 코드Pareto Set Learning for Neural Multi-objective Combinatorial Optimization
https://github.com/xuanzhouliu/d2match-icml23
공식 코드D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
https://github.com/xuehansheng/neurhap
공식 코드Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
https://github.com/xybfight/nar4tsp
공식 코드Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems
https://github.com/yd-kwon/MatNet
공식 코드Matrix Encoding Networks for Neural Combinatorial Optimization
https://github.com/yd-kwon/POMO
공식 코드POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
https://github.com/yd-kwon/sgbs
공식 코드Simulation-guided Beam Search for Neural Combinatorial Optimization
https://github.com/yhwang1990/code-bsm-release
공식 코드Balancing Utility and Fairness in Submodular Maximization (Technical Report)
https://github.com/yk7333/taskallocation
공식 코드A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation
https://github.com/yonxie/advfintweet
공식 코드A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
https://github.com/yuanhangzhang98/dmm_circuit
공식 코드Implementation of digital MemComputing using standard electronic components
https://github.com/yuanhangzhang98/memcomputing_3sat_demo
공식 코드https://github.com/yuansuny/tsp
공식 코드https://github.com/yunshengb/GraphSim
공식 코드Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
https://github.com/zakuta/rl-rqaoa-paper-code
공식 코드https://github.com/zdhnarsil/gflownet-combopt
공식 코드Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
https://github.com/zecevic-matej/hidden-confounder-attacks
공식 코드Structural Causal Models Reveal Confounder Bias in Linear Program Modelling
https://github.com/zehao-wang/indoortraj
공식 코드Diversity-Driven View Subset Selection for Indoor Novel View Synthesis
https://github.com/zguo235/bayesian_retro
공식 코드https://github.com/zhangzizhen/ml-dam
공식 코드Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
https://github.com/zhaoweicai/EdMIPS
공식 코드Rethinking Differentiable Search for Mixed-Precision Neural Networks
https://github.com/zhourunlong/RL-for-Combinatorial-Optimization
공식 코드Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization
https://github.com/zohairshafi/Graph-SCP
공식 코드Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.