Modified Bregman Iteration for Portfolio Optimization
نویسندگان
چکیده
We consider the l1-regularized Markowitz model, where a l1-penalty term is added to the objective function of the classical mean-variance one to stabilize the solution process, promoting sparsity in the solution and avoiding short positions. In this paper, we consider the Bregman iteration method to solve the related constrained optimization problem. We propose an iterative algorithm based on a modified Bregman iteration, in which an adaptive updating rule for the regularization parameter is defined. Our main result shows that the modified scheme preserves the properties of the original one. Numerical tests are reported, which show the effectiveness of the our approach.
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