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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 571580 of 1277 papers

TitleStatusHype
Convergence and Running Time of Time-dependent Ant Colony Algorithms0
Highly parallel algorithm for the Ising ground state searching problem0
HyColor: An Efficient Heuristic Algorithm for Graph Coloring0
IA-GM: A Deep Bidirectional Learning Method for Graph Matching0
Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm0
Artificial Catalytic Reactions in 2D for Combinatorial Optimization0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows0
Implicitly Intersecting Weighted Automata using Dual Decomposition0
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET0
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