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Barnes Maze Testing Strategies with Small and Large Rodent Models
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Greedy Methods, Randomization Approaches, and Multiarm Bandit Algorithms for Efficient Sparsity-Constrained

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    Area of Science:

    • Optimization Algorithms
    • Machine Learning
    • Signal Processing

    Background:

    • Sparsity-constrained algorithms like OMP and FW iteratively select variables by finding the maximal gradient component.
    • This gradient computation is computationally intensive for large-scale, high-dimensional data.

    Purpose of the Study:

    • To accelerate sparsity-constrained optimization algorithms.
    • To develop efficient methods for estimating the gradient's top entry.

    Main Methods:

    • Introduced greedy and randomization approaches to estimate the gradient's top entry.
    • Framed the problem as a best-arm identification in a multi-armed bandit (MAB) problem.
    • Developed a bandit-based algorithm for efficient top entry estimation.

    Main Results:

    • Proposed inexact FW and OMP algorithms that perform similarly to exact versions with high probability.
    • Demonstrated an order of magnitude acceleration using greedy deterministic and bandit approaches.
    • Experimental results show comparable efficiency to exact gradient methods in OMP, FW, and CoSaMP.

    Conclusions:

    • The proposed greedy and bandit methods significantly accelerate sparsity-constrained optimization.
    • These novel techniques offer efficient alternatives for large-scale and high-dimensional data processing.
    • The bandit-based approach provides a highly efficient way to estimate the top gradient entry.