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) is sufficiently reduced, for example, when . More generally, memory efficient and numerically stable schemes for damped least squares problems (e.g., LSQR) have been developed based on Lanczos bi-diagonalization ( Paige & Saunders 1982b). With these approaches memory requirements are minimal'only the most recent u k , v k need be retained, and solutions (and residuals) are updated at each step k . However, by actually saving all of U K , V K and B K (or in fact U K and J T U K ) it is possible to form and solve the small ( ) system (analogous to (12)) for any value of the regularization parameter . It is readily verified that the same error estimate (15) applies to this modified system. This approach, which allows an efficient implementation of the Occam scheme, is an example of a hybrid algorithm, of the sort previously discussed extensively in the numerical linear algebra literature ( O'Leary & Simmons 1981; Kilmer & O'Leary 2001; Hanke 2001). As for the standard Lanczos bi-diagonalization, the modified scheme generates a sequence of data space vectors which we denote as , subdivided into individual transmitter components as , with each now normalized separately so . As for standard Lanczos schemes the process is started from the right hand side of the system (5), but now with each block of the data vector normalized separately . Leaving aside for the moment how the vectors w jk are generated for k > 1, let be the matrix constructed from the first k subvectors for transmitter j , and define the block diagonal matrix. Pseudo-code for the modified hybrid scheme is given in Fig. Apostar online na loteria.Best No Wagering Casinos.
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