2025/10/22 by Rufeng Xiao, Xiao, Rufeng, Zhiping Li +3
Computer Science · Decision Sciences · Engineering · #FOS: Mathematics #Optimization and Control (math.OC) #Risk and Portfolio Optimization #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2510.19614
openalex publication_date 2025/10/22 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
Utility-based shortfall risk (UBSR), a convex risk measure sensitive to tail losses, has gained popularity in recent years. However, research on computational methods for UBSR optimization remains relatively scarce. In this paper, we propose a fast and scalable algorithm for the UBSR-based portfolio optimization problem. Leveraging the Sample Average Approximation (SAA) framework, we reformulate the problem as a block-separable convex program and solve it efficiently via the alternating direction method of multipliers (ADMM). In the high-dimensional setting, a key challenge arises in one of the subproblems -- a projection onto a nonlinear feasibility set defined by the shortfall-risk constraint. We propose two semismooth Newton algorithms to solve this projection subproblem. The first algorithm directly applies a semismooth Newton iteration to the Karush-Kuhn-Tucker (KKT) system of the projection problem. The second algorithm employs an implicit function transformation of semismooth functions to reduce the problem to a univariate equation involving the Lagrange multiplier and achieves global superlinear convergence with enhanced numerical stability under mild regularity conditions. Theoretical convergence guarantees of the proposed algorithms are established, and numerical experiments demonstrate a substantial speedup over state-of-the-art solvers, particularly in high-dimensional regimes.