Inertial Proximal Block Coordinate Method for a Class of Nonsmooth Sum-of-Ratios Optimization Problems

Abstract

In this paper, we consider a class of nonsmooth sum-of-ratios fractional optimization problems with block structure. This model class is ubiquitous and encompasses several important nonsmooth optimization problems in the literature. We first propose an inertial proximal block coordinate method for solving this class of problems by exploiting the underlying structure. The global convergence of our method is guaranteed under the Kurdyka--Lojasiewicz (KL) property and some mild assumptions. We then identify the explicit exponents of the KL property for three important structured fractional optimization problems. In particular, for the sparse generalized eigenvalue problem with either cardinality regularization or sparsity constraint, we show that the KL exponents are 1/2, and so, the proposed method exhibits linear convergence rate. Finally, we illustrate our theoretical results with both analytic and simulated numerical examples.

References in corpus (4)

• Fractional Programming for Communication Systems--Part I: Power Control and Beamforming
• Calculus of the exponent of Kurdyka-Łojasiewicz inequality and its applications to linear convergence of first-order methods
• Sparse Generalized Eigenvalue Problem via Smooth Optimization
• Extrapolated Proximal Subgradient Algorithms for Nonconvex and Nonsmooth Fractional Programs

Cited by in corpus (2)

• Bregman Proximal Linearized ADMM for Minimizing Separable Sums Coupled by a Difference of Functions
• A proximal subgradient algorithm with extrapolation for structured nonconvex nonsmooth problems