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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

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Showing 1011–1020 of 1277 papers

TitleStatusHype
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning—0
DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter—0
Online learning for min-max discrete problems—0
Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location—0
Highly parallel algorithm for the Ising ground state searching problem—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice—0
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments—0
Co-training for Policy LearningCode0
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference—0
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