Coordinate Descent Methods for Fractional Minimization
This work addresses optimization challenges in machine learning and signal processing, offering incremental improvements for specific fractional minimization tasks.
The authors tackled non-convex fractional minimization problems with structured objectives by proposing two coordinate descent methods, which converge to coordinate-wise stationary points and, under certain conditions, achieve global minima with proven convergence rates, outperforming existing methods in accuracy on real-world data.
We consider a class of structured fractional minimization problems, in which the numerator part of the objective is the sum of a differentiable convex function and a convex non-smooth function, while the denominator part is a convex or concave function. This problem is difficult to solve since it is non-convex. By exploiting the structure of the problem, we propose two Coordinate Descent (CD) methods for solving this problem. The proposed methods iteratively solve a one-dimensional subproblem \textit{globally}, and they are guaranteed to converge to coordinate-wise stationary points. In the case of a convex denominator, under a weak \textit{locally bounded non-convexity condition}, we prove that the optimality of coordinate-wise stationary point is stronger than that of the standard critical point and directional point. Under additional suitable conditions, CD methods converge Q-linearly to coordinate-wise stationary points. In the case of a concave denominator, we show that any critical point is a global minimum, and CD methods converge to the global minimum with a sublinear convergence rate. We demonstrate the applicability of the proposed methods to some machine learning and signal processing models. Our experiments on real-world data have shown that our method significantly and consistently outperforms existing methods in terms of accuracy.