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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 461470 of 1277 papers

TitleStatusHype
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm0
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems0
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization0
Box Facets and Cut Facets of Lifted Multicut Polytopes0
Towards Principled Task Grouping for Multi-Task Learning0
Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities0
Reasoning Algorithmically in Graph Neural Networks0
RITFIS: Robust input testing framework for LLMs-based intelligent software0
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization0
Convergence Acceleration of Markov Chain Monte Carlo-based Gradient Descent by Deep Unfolding0
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