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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 411–420 of 1277 papers

TitleStatusHype
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems—0
Efficient Training of Multi-task Combinarotial Neural Solver with Multi-armed Bandits—0
Embed and Project: Discrete Sampling with Universal Hashing—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Budgeted Influence Maximization for Multiple Products—0
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location—0
End-to-end Planning of Fixed Millimeter-Wave Networks—0
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization—0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
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