A course of higher mathematics, vol. 1 by Smirnov V.I.

By Smirnov V.I.

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E4 denote the columns of 14. 2) is in upper echelon form. In fact, since P2 is chosen so that it interchanges the second row of EIP1A with its third or fourth row, if necessary, and P3 is chosen so that it interchanges the third row of E2P2EIPIA with its fourth row, if necessary, these two permutation matrices have a special form: P2 - [01 °IITl] , where III P3 = [~ where 112 is a 2 x 2 permutation matrix. 1. Gaussian elimination redux where Ej denotes a matrix of the same form as E j • Thus, for example, since e~ P3 = e~ and V2 = P3U2 is a vector of the same form as U2, it follows that P3 E 2 P3(14 + u2e f) P3 +v2e~ = P3 + v2e~ P3 = E~P3 , where E~ = 14 +v2e~ is a matrix of the same form as E 2 • In a similar vein P3P2E I = P3E~ P2 = E~ P3P2 and consequently, E3P3E2P2EIPI = E3E~E~ P3P2PI = EP, with E = E3E~E~ and P = P3P2PI .

Show that . , the span of the pivot columns of A. 2. 3. Upper echelon matrices The examples in the preceding section serve to illustrate the central role played by the number of pivots in an upper echelon matrix U and their positions when trying to solve systems of equations by Gaussian elimination. Our next main objective is to exploit the special structure of upper echelon matrices in order to draw some general conclusions for matrices in this class. 4. Let A E lF pxq and assume that A i= Opxq.

Oscar Wilde This chapter is devoted to a number of applications of Gaussian elimination, both theoretical and computational. There is some overlap with conclusions reached in the preceding chapter, but the methods of obtaining them are usually different. 1. 1. Let A E lF pxq be a nonzero matrix. Then there exists a set of lower triangular p x p matrices EI, ... ,Ek with ones on the diagonal and a set of p x p permutation matrices PI, ... 1) is in upper echelon form. Moreover, in this formula, Pj acts only (if at all) on rows j, ...

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