Sayed, Ali H.Nascimento, V. H.Cipparrone, F. A. M.2017-12-192017-12-192017-12-19200210.1137/S0895479800380799https://infoscience.epfl.ch/handle/20.500.14299/142903This paper formulates and solves a robust criterion for least-squares designs in the presence of uncertain data. Compared with earlier studies, the proposed criterion incorporates simultaneously both regularization and weighting and applies to a large class of uncertainties. The solution method is based on reducing a vector optimization problem to an equivalent scalar minimization problem of a provably unimodal cost function, thus achieving considerable reduction in computational complexity.text::journal::journal article::research article