Stochastic Variance-Reduced Iterative Hard Thresholding in Graph Sparsity Optimization
<|reference_start|>Stochastic Variance-Reduced Iterative Hard Thresholding in Graph Sparsity Optimization: Stochastic optimization algorithms are widely used for large-scale data analysis due to their low per-iteration costs, but they often suffer from slow asymptotic convergence caused by inherent variance. Variance-reduced techniques have been therefore used to address this issue in structured sparse models utilizing sparsity-inducing norms or $\ell_0$-norms. However, these techniques are not directly applicable to complex (non-convex) graph sparsity models, which are essential in applications like disease outbreak monitoring and social network analysis. In this paper, we introduce two stochastic variance-reduced gradient-based methods to solve graph sparsity optimization: GraphSVRG-IHT and GraphSCSG-IHT. We provide a general framework for theoretical analysis, demonstrating that our methods enjoy a linear convergence speed. Extensive experiments validate<|reference_end|>
@article{fox2024stochastic,
    title={Stochastic Variance-Reduced Iterative Hard Thresholding in Graph
  Sparsity Optimization},
    author={Derek Fox and Samuel Hernandez and Qianqian Tong},
    journal={arXiv preprint arXiv:2407.16968},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16968},
    primaryClass={stat.ML cs.AI cs.LG}
}