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Guided Combinatorial Algorithms for Submodular Maximization
For constrained, not necessarily monotone submodular maximization, guiding the measured continuous greedy algorithm with a local search algorithm currently obtains the state-of-the-art approximation factor of 0. 401 \citep{buchbinder2023constrained}.
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization.
Dual-CyCon Net: A Cycle Consistent Dual-Domain Convolutional Neural Network Framework for Detection of Partial Discharge
This loss explores cycle-invariant PD-specific features, enabling the model to learn more robust, noise-invariant features for PD detection.
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