Fast gradient descent method for Mean-CVaR optimization
We propose an iterative gradient descent algorithm for solving scenario-based Mean-CVaR portfolio selection problem. The algorithm is fast and does not require any LP solver. It also has efficiency advantage over the LP approach for large scenario size.
conditional value-at-risk portfolio optimization