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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

Showing 711720 of 1277 papers

TitleStatusHype
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems0
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set0
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process0
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees0
Rethinking Selection in Generational Genetic Algorithms to Solve Combinatorial Optimization Problems: An Upper Bound-based Parent Selection Strategy for Recombination0
Revealed Preferences of One-Sided Matching0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning0
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?0
RIGA: A Regret-Based Interactive Genetic Algorithm0
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