Robust Solutions to Least-Squares Problems with Uncertain Data
Robust Solutions to Least-Squares Problems with Uncertain Data
We consider least-squares problems where the coefficient matrices A,b are unknown but bounded. We minimize the worst-case residual error using (convex) second-order cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpreted as a Tikhonov regularization procedure, with the advantage …