https://github.com/PotatoJnny/ReSCO
공식 코드Reheated Gradient-based Discrete Sampling for 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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding Process
Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems
Estimating the stability number of a random graph using convolutional neural networks
Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
Adversarial Immunization for Certifiable Robustness on Graphs
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
Submodular Batch Selection for Training Deep Neural Networks
Controlling Continuous Relaxation for Combinatorial Optimization
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach
Approximation Algorithms for Combinatorial Optimization with Predictions
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
Hybrid Pointer Networks for Traveling Salesman Problems Optimization
Efficient Active Search for Combinatorial Optimization Problems
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.