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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 951960 of 1277 papers

TitleStatusHype
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction0
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network0
Multidimensional Assignment Problem for multipartite entity resolution0
Multi-IRS Enhanced Wireless Coverage: Deployment Optimization Based on Large-Scale Channel Knowledge0
Multi-layer local optima networks for the analysis of advanced local search-based algorithms0
Multi-objective Evolution of Heuristic Using Large Language Model0
Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off0
Multiple-gain Estimation for Running Time of Evolutionary Combinatorial Optimization0
Sparse Multi-Reference Alignment : Phase Retrieval, Uniform Uncertainty Principles and the Beltway Problem0
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem0
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