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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 421430 of 1277 papers

TitleStatusHype
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent0
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach0
Transformers in Reinforcement Learning: A Survey0
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF SignalsCode0
A Graph Multi-separator Problem for Image Segmentation0
Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization0
Large Language Models for Supply Chain Optimization0
Explainable quantum regression algorithm with encoded data structure0
Learning to Branch in Combinatorial Optimization with Graph Pointer Networks0
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
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