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A Single-loop Proximal Subgradient Algorithm for A Class Structured Fractional Programs
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DOI：10.1007/s10915-025-02987-x.png)
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Abstract
En 中文
In this paper, we investigate a class of nonconvex and nonsmooth fractional programming problems, where the numerator composed of two parts: a convex, nonsmooth function and a differentiable, nonconvex function, and the denominator consists of a convex, nonsmooth function composed of a linear operator. These structured fractional programming problems have broad applications, including CT reconstruction, sparse signal recovery, the single-period optimal portfolio selection problem and standard Sharpe ratio minimization problem. We develop a single-loop proximal subgradient algorithm that alleviates computational complexity by decoupling the evaluation of the linear operator from the nonsmooth component. We prove the global convergence of the proposed single-loop algorithm to an exact lifted stationary point under the Kurdyka-Łojasiewicz assumption. Additionally, we present a practical variant incorporating a nonmonotone line search to improve computational efficiency. Finally, through extensive numerical simulations, we showcase the superiority of the proposed approach over the existing state-of-the-art methods for three applications: $$L_{1}/S_{\kappa }$$ sparse signal recovery, limited-angle CT reconstruction, and optimal portfolio selection.
Keywords:
Fractional programming
Single-loop
Convergence analysis
Decoupling
Journal
IF:
3.3
Papers:
655
Citations:
9.6K