R q p qq tr p tr qq tr q q r x matrixrnorm1553 y qrx

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R = Q P = QQ . tr P = tr QQ = tr Q Q = r .
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> x<-matrix(rnorm(15),5,3) > y<-qr(x) > y $qr [,1] [,2] [,3] [1,] -1.72567945 -0.5367561 -0.14277359 [2,] 0.06905176 1.1188063 -0.06029126 [3,] 0.27293972 -0.6187797 0.44352349 [4,] -0.45991800 -0.1067149 0.18713748 [5,] -0.44927581 0.5819942 0.78630026 $rank [1] 3 $qraux [1] 1.712294 1.516727 1.588822 $pivot [1] 1 2 3 attr(,"class") [1] "qr" 67
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> qr.Q(y) [,1] [,2] [,3] [1,] -0.71229379 -0.3456045 -0.26525534 [2,] -0.06905176 -0.5306637 0.06260371 [3,] -0.27293972 0.5636904 -0.66100833 [4,] 0.45991800 0.1995434 -0.12104787 [5,] 0.44927581 -0.4913137 -0.68857518 > qr.R(y) [,1] [,2] [,3] [1,] -1.725679 -0.5367561 -0.14277359 [2,] 0.000000 1.1188063 -0.06029126 [3,] 0.000000 0.0000000 0.44352349 68
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6: LDU Decomposition and Matrix Inverse 69
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In this chapter we will re-define the rank and the inverse of square matrices, using the LDU decomposition . In a matrix decomposition a matrix is written as a product of simpler matrices. This gives information about the matrix and can be used to guide subsequent computations. This is like decomposing a polynomial into factors, which shows what the roots and the general properties of the polynomial are. 70
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LDU decomposition Each square matrix A can be written as a product A=LDU , where L is lower-triangular, U is upper- triangular, and D is diagonal. We can choose L and U such that they have ones on the diagonal (and are thus non-singular). 71 a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33 = 1 0 0 l 21 1 0 l 31 l 32 1 d 11 0 0 0 d 22 0 0 0 d 33 1 u 12 u 13 0 1 u 23 0 0 1 .
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More in detail: We can easily solve for the unknowns from here. NB: this process can go astray (suppose for instance a 11 =0 and a 12 0 ). We then need to pivot on off- diagonal elements (and use permutation matrices). 72 1 0 0 l 21 1 0 l 31 l 32 1 d 11 0 0 0 d 22 0 0 0 d 33 1 u 12 u 13 0 1 u 23 0 0 1 = = d 11 d 11 u 12 d 11 u 13 d 11 l 21 d 22 + d 11 l 21 u 12 d 11 l 21 u 13 + d 22 u 23 d 11 l 31 d 11 l 31 u 12 + d 22 l 32 d 11 l 31 u 13 + d 22 l 32 u 23 + d 33
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In matrix-vector notation LDU can also be written as follows. If A is a square matrix and e i is a unit vector (a vector with element i equal to one and zero elsewhere), then Ae i is column i of A and e i 'A is row i . Now compute This matrix has both row i and column i equal to zero. 73 A - Ae i e i A e i Ae i
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If we apply this result recursively along the diagonal we get a decomposition of the form and so on, which can be written as with L lower triangular with unit diagonal, U upper triangular with unit diagonal, and with D diagonal. 74 A = Ae 1 e 1 A e 1 Ae 1 + A 1 , A 1 = A 1 e 2 e 2 A 1 e 2 A 1 e 2 + A 2 , A = LDU
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pivot<-function(x,i,j) { p<-x[i,j]; r<-x[i,]/p; c<-x[,j]/p return(x-p*outer(c,r)) } pivot_along_diagonal<-function(x) { n<-nrow(x); d<-diag(n);l<-r<-matrix(0,n,n) for (i in 1:n) { p<-x[i,i]; u<-x[i,]/p; v<-x[,i]/p x<-x-p*outer(v,u) l[,i]<-v; r[i,]<-u;d[i,i]<-p } return(list(l=l,r=r,d=d)) } 75
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pivot <- function ( x ) { n <- nrow ( x ); m <- ncol ( x ); ii <- rep ( 0 , n ); jj <- rep ( 0 , m ) l <- u <- d <- matrix ( 0 , n , m ); k <- 1 repeat { print ( x ) if ( substr ( readline ( "Continue ? " ), 1 , 1 ) == "n" ) break () i <- as.integer ( readline ( "Row Index ? " )) j <- as.integer ( readline ( "Column Index ? " )) ii [ k ]<- i ; jj [ k ]<- j p <- x [ i , j ]; r <- x [ i ,]/ p ; c <- x [, j ]/ p x <- x - p * outer ( c , r ) l [, i ]<- c ; u [ j ,]<- r ; d [ i , i ]<- p k <- k + 1 } return ( list ( l = l , u = u , d = d )) } 76
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> a [,1] [,2] [,3] [1,] -0.17 0.15 0.46 [2,] 0.92 -0.57 0.95 [3,] -1.89 -0.41 -1.22 > a1 [,1] [,2] [,3] [1,] 0 0.00 0.0 [2,] 0 0.26 3.4 [3,] 0 -2.12 -6.3 > a2 [,1] [,2] [,3] [1,] 0 0 0 [2,] 0 0 0 [3,] 0 0 22 77
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> l l1 l2 l3 [1,] 1.0 0.0 0 [2,] -5.4 1.0 0 [3,] 11.1 -8.2 1 > u u1 u2 u3 [1,] 1.0 0 0 [2,] -0.9 1 0 [3,] -2.7 13 1 > d [,1] [,2] [,3] [1,] -0.17 0.00 0 [2,] 0.00 0.26 0 [3,] 0.00 0.00 22 78
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For symmetric matrices the LDU decomposition becomes the LDL' decomposition. The Cholesky decomposition is the LDL' decomposition that can applied to positive semi-definite matrices. It
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