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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 151160 of 1277 papers

TitleStatusHype
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
Combinatorial Optimization Perspective based Framework for Multi-behavior RecommendationCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Quantum approximate optimization via learning-based adaptive optimizationCode1
Exploring the Power of Graph Neural Networks in Solving Linear Optimization ProblemsCode1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
A Bayesian algorithm for retrosynthesisCode1
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
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