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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 751–760 of 1277 papers

TitleStatusHype
Self-Improved Learning for Scalable Neural Combinatorial Optimization—0
Self-Supervised Penalty-Based Learning for Robust Constrained Optimization—0
Semantic Dependency Parsing via Book Embedding—0
Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World—0
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization—0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning—0
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
Set-valued prediction in hierarchical classification with constrained representation complexity—0
Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation—0
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
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