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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 961970 of 1277 papers

TitleStatusHype
An Introduction to Quantum Machine Learning for Engineers0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization0
Annealed Training for Combinatorial Optimization on Graphs0
Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment0
A novel channel pruning method for deep neural network compression0
A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning0
An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem0
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