https://github.com/lyeskhalil/mipgnn
공식 코드MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
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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.
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
Artificial Potential Field-Based Path Planning for Cluttered Environments
Combinatorial optimization of the coefficient of determination
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
HeurAgenix: Leveraging LLMs for Solving Complex Combinatorial Optimization Challenges
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic Programming
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
CLIPPER: A Graph-Theoretic Framework for Robust Data Association
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Variational Annealing on Graphs for Combinatorial Optimization
Decision-Focused Learning to Predict Action Costs for Planning
Synthesizing Interpretable Control Policies through Large Language Model Guided Search
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
GOAL: A Generalist Combinatorial Optimization Agent Learning
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Efficient Parallel Genetic Algorithm for Perturbed Substructure Optimization in Complex Network
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing
RouteExplainer: An Explanation Framework for Vehicle Routing Problem
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.