Indirect Methods for Personalized Mean-CVaR Portfolio Optimization - Setyawan, Deva Putra; Salih, Yasir (Operations Research: International Conference Series, 2025)
This study presents a reformulation of the Personalized Mean-CVaR model into an unconstrained optimization problem, which is then solved using iterative methods, including steepest descent and Newton’s method. The reformulation introduces challenges related to feasibility region checking, convexity of the feasible set and objective functions, and the use of Lagrange multipliers to handle constraints. Additionally, Taylor expansion is utilized to approximate the objective function in each iteration. The research evaluates the effectiveness of iterative optimization techniques in solving the Personalized Mean-CVaR problem, while addressing key challenges in convergence and stability of the solution.