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- W2006142473 abstract "This paper investigates optimality properties of central and projection algorithms for linear problems in the field of system identification in a context in which uncertainty is described in a deterministic rather than statistical way. Particular attention is devoted to least-squares algorithms when the measurement noise is assumed to be unknown but bounded in a Hilbert norm. A major contribution of this paper consists in proving that least-squares algorithms enjoy strong optimality properties. On the contrary, it is pointed out that these properties do not hold for other frequently used projection algorithms, such as least-absolute-values or minimax algorithms, corresponding to a description of the measurement error in h or l∞ norm, respectively." @default.
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- W2006142473 date "1986-12-01" @default.
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- W2006142473 title "Optimality of central and projection algorithms for bounded uncertainty" @default.
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- W2006142473 doi "https://doi.org/10.1016/0167-6911(86)90075-7" @default.
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