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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 261270 of 1277 papers

TitleStatusHype
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Graph Adversarial Immunization for Certifiable RobustnessCode0
Implementation of digital MemComputing using standard electronic componentsCode0
An ant colony optimization algorithm for job shop scheduling problemCode0
Learning to Perform Local Rewriting for Combinatorial OptimizationCode0
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF SignalsCode0
Learning Interpretable Error Functions for Combinatorial Optimization Problem ModelingCode0
Automated quantum programming via reinforcement learning for combinatorial optimizationCode0
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex NetworksCode0
Fast Parallel Algorithms for Statistical Subset Selection ProblemsCode0
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