2025/01/31 by Arturo Annunziata, Annunziata, Arturo, Matteo Lapucci +5
Decision Sciences · Economics, Econometrics and Finance · Engineering · #90B50 #90C26 #90C29 #91G10 #FOS: Mathematics #Optimization and Control (math.OC) #Reservoir Engineering and Simulation Methods #Risk and Portfolio Optimization #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2501.19199
openalex publication_date 2025/01/31 · openalex created_date 2025/02/04 · openalex updated_date 2026/07/28
In this work, we deal with the problem of computing a comprehensive front of efficient solutions in multi-objective portfolio optimization problems in presence of sparsity constraints. We start the discussion pointing out some weaknesses of the classical linear scalarization approach when applied to the considered class of problems. We are then motivated to propose a suitable algorithmic framework that is designed to overcome these limitations: the novel algorithm combines a gradient-based exploration-refinement strategy with a tailored initialization scheme based on memetic or multi-start descent procedures. Thorough computational experiments highlight how the proposed method is far superior to both linear scalarization and popular genetic algorithms.