𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Toward the kernel of the vector epsilon algorithm

✍ Scribed by John A. Steele; Allan T. Dolovich


Publisher
John Wiley and Sons
Year
2000
Tongue
English
Weight
98 KB
Volume
48
Category
Article
ISSN
0029-5981

No coin nor oath required. For personal study only.

✦ Synopsis


The vector epsilon algorithm (VEA) is a non-linear sequence-to-sequence transformation which has been in use for over 35 years to determine the limits (antilimits) of convergent (divergent) vector sequences. Recently, it has been used in a variety of engineering applications to accelerate iterative solution processes, including iterative ΓΏnite element techniques. The VEA has been shown to give the limiting value of many sequences. However, an expression describing the kernel of the VEA, the set of all sequences {vn} which the VEA extrapolates successfully to the sequence's limit (antilimit) vector v, remains elusive. Here, this question is addressed with a simple proof giving the kernel of the ΓΏrst-order VEA with some comments about the kernel for higher orders. We prove that the ΓΏrst-order VEA assumes that each term of the related sequence {vn -v} is rotated by a ΓΏxed angle and scaled in length by a constant factor with respect to the preceding term.


πŸ“œ SIMILAR VOLUMES


The kernel algorithm for PLS
✍ Fredrik Lindgren; Paul Geladi; Svante Wold πŸ“‚ Article πŸ“… 1993 πŸ› John Wiley and Sons 🌐 English βš– 833 KB
Comments on the PLS kernel algorithm
✍ Sijmen De Jong; Cajo J. F. Ter Braak πŸ“‚ Article πŸ“… 1994 πŸ› John Wiley and Sons 🌐 English βš– 355 KB

Lindgren et al. ( J . Chemometrics, 7 , 45-59 (1993)) published a so-called kernel algorithm for PLS regression of Y against X when the number of objects is very large. The algorithm is based solely on deflation of the cross-product matrices XTX, YTY and XTY. The algorithm is now described in a shor

On the fast search algorithms for vector
✍ Wen-Shiung Chen; Lili Hsieh; Shang-Yuan Yuan πŸ“‚ Article πŸ“… 2002 πŸ› John Wiley and Sons 🌐 English βš– 128 KB

## Abstract One of the major difficulties arising in vector quantization (VQ) is high encoding time complexity. Based on the well‐known partial distance search (PDS) method and a special order of codewords in VQ codebook, two simple and efficient methods are introduced in fast full search vector qu