Publication
This 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.
Stefana Parascho, Pierluigi D'Acunto
Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi
Colin Neil Jones, Yuning Jiang, Yingzhao Lian, Xinliang Dai