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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 11511160 of 1277 papers

TitleStatusHype
Implementation of digital MemComputing using standard electronic componentsCode0
A Survey and Analysis of Evolutionary Operators for PermutationsCode0
Simulation Based Bayesian OptimizationCode0
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman ProblemCode0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial OptimizationCode0
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
Black-box Combinatorial Optimization using Models with Integer-valued MinimaCode0
Curriculum Learning for Cumulative Return MaximizationCode0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
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