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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 11911200 of 1277 papers

TitleStatusHype
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms0
Exact and Approximate Hierarchical Clustering Using A*0
Exact and heuristic methods for the discrete parallel machine scheduling location problem0
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks0
Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning0
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment0
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms0
Experiments with graph convolutional networks for solving the vertex p-center problem0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application0
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