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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
Deep Auto-Deferring Policy for Combinatorial Optimization0
Deep Causal Learning: Representation, Discovery and Inference0
DeepCO: Offline Combinatorial Optimization Framework Utilizing Deep Learning0
DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter0
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks0
Deep Generative Model for Mechanical System Configuration Design0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems0
Deep Learning of Graph Matching0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem0
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