Text
Distributionally Robust Second-Order Stochastic Dominance Constrained Optimization with Wasserstein Ball
We consider a distributionally robust second-order stochastic dominance constrained optimization problem. We require the dominance constraints to hold with respect to all probability distributions in a Wasserstein ball centered at the empirical distribution. We adopt the sample approximation approach to develop a linear programming formulation that provides a lower bound. We propose a novel split-and-dual decomposition framework which provides an upper bound. We establish quantitative convergence for both lower and upper approximations given some constraint qualification conditions. To efficiently solve the nonconvex upper bound problem, we use a sequential convex approximation algorithm. Numerical evidence on a portfolio selection problem validates the convergence and effectiveness of the proposed two approximation methods.
Barcode | Tipe Koleksi | Nomor Panggil | Lokasi | Status | |
---|---|---|---|---|---|
art142449 | null | Artikel | Gdg9-Lt3 | Tersedia namun tidak untuk dipinjamkan - No Loan |
Tidak tersedia versi lain