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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 691–700 of 1277 papers

TitleStatusHype
Recursive Decomposition for Nonconvex Optimization—0
RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models—0
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools—0
Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection—0
Regularized Greedy Column Subset Selection—0
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
Reinforcement learning based local search for grouping problems: A case study on graph coloring—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
Reinforcement Learning Driven Heuristic Optimization—0
Reinforcement Learning Enhanced Explainer for Graph Neural Networks—0
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