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Combinatorial Optimization

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.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 1251–1277 of 1277 papers

TitleStatusHype
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective—0
Graph Ordering: Towards the Optimal by Learning—0
Graph Q-Learning for Combinatorial Optimization—0
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application—0
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective—0
GraphThought: Graph Combinatorial Optimization with Thought Generation—0
GRASP: Accelerating Shortest Path Attacks via Graph Attention—0
Greedy-Based Feature Selection for Efficient LiDAR SLAM—0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Green Heron Swarm Optimization Algorithm - State-of-the-Art of a New Nature Inspired Discrete Meta-Heuristics—0
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning—0
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs—0
Gumbel-softmax Optimization: A Simple General Framework for Combinatorial Optimization Problems on Graphs—0
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization—0
Hardness of Online Sleeping Combinatorial Optimization Problems—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Heuristic with elements of tabu search for Truck and Trailer Routing Problem—0
Arbitrarily Large Labelled Random Satisfiability Formulas for Machine Learning Training—0
Hierarchical Clustering: Objective Functions and Algorithms—0
High-Dimensional Prediction for Sequential Decision Making—0
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability—0
Higher-Order Quantum-Inspired Genetic Algorithms—0
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET—0
Highly parallel algorithm for the Ising ground state searching problem—0
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware—0
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem—0
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems—0
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