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A regularized robust design criterion for uncertain data. (English) Zbl 1058.15002

Summary: 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.

MSC:

15A06 Linear equations (linear algebraic aspects)
90C47 Minimax problems in mathematical programming

Software:

VanHuffel
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