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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 871–880 of 1277 papers

TitleStatusHype
WeaveNet: A Differentiable Solver for Non-linear Assignment Problems—0
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization—0
When can l_p-norm objective functions be minimized via graph cuts?—0
WiSM: Windowing Surrogate Model for Evaluation of Curvature-Constrained Tours with Dubins vehicle—0
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference—0
XPrompt:Explaining Large Language Model's Generation via Joint Prompt Attribution—0
YaoGAN: Learning Worst-case Competitive Algorithms from Self-generated Inputs—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
Robust Metric Learning by Smooth Optimization—0
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems—0
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