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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 311320 of 1277 papers

TitleStatusHype
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems0
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization0
Box Facets and Cut Facets of Lifted Multicut Polytopes0
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot GeneralizationCode1
Towards Principled Task Grouping for Multi-Task Learning0
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities0
RITFIS: Robust input testing framework for LLMs-based intelligent software0
Reasoning Algorithmically in Graph Neural Networks0
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding0
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