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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 621–630 of 1277 papers

TitleStatusHype
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems—0
Learning to Reduce Search Space for Generalizable Neural Routing Solver—0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem—0
Bayesian Meta-Prior Learning Using Empirical Bayes—0
Continuous Latent Search for Combinatorial Optimization—0
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization—0
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools—0
Large Language Models for Combinatorial Optimization of Design Structure Matrix—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Hardness of Online Sleeping Combinatorial Optimization Problems—0
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