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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 961970 of 1277 papers

TitleStatusHype
Sparse Optimization for Green Edge AI Inference0
Learning Interpretable Error Functions for Combinatorial Optimization Problem ModelingCode0
Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives0
Fair Correlation ClusteringCode0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning0
Bayesian Meta-Prior Learning Using Empirical Bayes0
WiSM: Windowing Surrogate Model for Evaluation of Curvature-Constrained Tours with Dubins vehicle0
A Class of Linear Programs Solvable by Coordinate-Wise Minimization0
On the Performance of Metaheuristics: A Different Perspective0
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