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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 281290 of 1277 papers

TitleStatusHype
Robust Metric Learning by Smooth Optimization0
Deep Generative Model for Mechanical System Configuration Design0
Binary sequence set optimization for CDMA applications via mixed-integer quadratic programming0
Binary matrix factorization on special purpose hardware0
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules0
A Distribution Evolutionary Algorithm for the Graph Coloring Problem0
Accelerating Quantum Approximate Optimization Algorithm using Machine Learning0
BiGrad: Differentiating through Bilevel Optimization Programming0
Biased Random-Key Genetic Algorithms: A Review0
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