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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 251260 of 1277 papers

TitleStatusHype
A Diffusion Model Framework for Unsupervised Neural Combinatorial OptimizationCode2
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits0
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with PerspectivesCode2
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement0
XPrompt:Explaining Large Language Model's Generation via Joint Prompt Attribution0
A random-key GRASP for combinatorial optimizationCode0
Metaheuristics and Large Language Models Join Forces: Towards an Integrated Optimization Approach0
Network Interdiction Goes Neural0
ProDAG: Projected Variational Inference for Directed Acyclic GraphsCode0
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