Fast gradient descent method for Mean-CVaR optimization
نویسندگان
چکیده
We propose an iterative gradient descent procedure for computing approximate solutions for the scenario-based mean-CVaR portfolio selection problem. This procedure is based on an algorithm proposed by Nesterov [13] for solving non-smooth convex optimization problems. Our procedure does not require any linear programming solver and in many cases the iterative steps can be solved in closed form. We show that this method is significantly superior to the linear programming approach as the number of scenarios becomes large.
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ورودعنوان ژورنال:
- Annals OR
دوره 205 شماره
صفحات -
تاریخ انتشار 2013