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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 11011110 of 1277 papers

TitleStatusHype
Composing photomosaic images using clustering based evolutionary programming0
Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection0
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form0
Cakewalk Sampling0
Memcomputing: Leveraging memory and physics to compute efficiently0
Machine Learning Methods for Data Association in Multi-Object Tracking0
Discrepancy-based Evolutionary Diversity Optimization0
Graph2Seq: Scalable Learning Dynamics for Graphs0
Reinforcement Learning for Solving the Vehicle Routing ProblemCode0
When can l_p-norm objective functions be minimized via graph cuts?0
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