Скачать презентацию Householder Transformations Example DEF is called Householder matrix Скачать презентацию Householder Transformations Example DEF is called Householder matrix

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Householder Transformations Example: DEF: is called Householder matrix ( Reflection, Transformation) Householder Transformations Example: DEF: is called Householder matrix ( Reflection, Transformation)

Householder Transformations Example DEF: is called Householder matrix ( Reflection, Transformation) Rem: 1) They Householder Transformations Example DEF: is called Householder matrix ( Reflection, Transformation) Rem: 1) They are rank-1 modifications of the identity 2) They are symmetric and orthogonal 3) They can be used to zero selected components of a vector Example

QR factorization A=QR = Orthogonal Upper triangular We begin with a QR factorization method QR factorization A=QR = Orthogonal Upper triangular We begin with a QR factorization method that utilizes Householder transformations.

QR factorization Upper triangular QR factorization Upper triangular

QR factorization Upper triangular Remark: Product of orthogonal matrices is an orthogonal QR factorization Upper triangular Remark: Product of orthogonal matrices is an orthogonal

Householder Transformations DEF: is called Householder matrix ( Reflection, Transformation) Example (left multiplication) Householder Transformations DEF: is called Householder matrix ( Reflection, Transformation) Example (left multiplication)

Computing Householder Choice of sign: It is dangerous if x is close to a Computing Householder Choice of sign: It is dangerous if x is close to a positive multiple of e 1 because sever cancellation would occur. Solution:

Applying Householder matrices never formed explicitly # of multiplications # of Additions Applying Householder matrices never formed explicitly # of multiplications # of Additions

Householder Transformations Matlab [Q, R] = qr(A), where A is m-by-n, produces an m-by-n Householder Transformations Matlab [Q, R] = qr(A), where A is m-by-n, produces an m-by-n upper triangular matrix R and an m-by-m unitary matrix Q so that A = Q*R.

Reduced QR factorization = Orthogonal = Orthog onal Upper triangular Reduced QR factorization = Orthogonal = Orthog onal Upper triangular

Gram-Schmedit Gram-schmedit Orthogonalization Gram-Schmedit Gram-schmedit Orthogonalization

Gram-Schmedit Gram-Schmedit

Gram-Schmedit Example: Gram-Schmedit Example:

Givens Matrices Rotation Example: Givens Matrices Rotation Example:

Givens Rotations To zero a specific entry (not all as Householder) Givens Rotations are Givens Rotations To zero a specific entry (not all as Householder) Givens Rotations are of this form: We can force by setting: Givens Rotation are orthogonal to be zero

Householder Transformations Example: Applying Givens Rotations # of operations = 6 n Just effects Householder Transformations Example: Applying Givens Rotations # of operations = 6 n Just effects two rows of A

Householder Transformations Givens QR Householder Transformations Givens QR

Householder Transformations Theorem: (QR Decomposition) If A is real m-by-n matrix, then there exist Householder Transformations Theorem: (QR Decomposition) If A is real m-by-n matrix, then there exist orthogonal matrix Q such that Theorem: (QR Decomposition) If A is real m-by-n matrix of full rank, then A has a unique reduced QR factorization

Householder Transformations function [v]=house(x) v=x; v(1)=sign(x(1))*norm(x)+x(1); function [Q, R]=myqr(A) [m, n]=size(A); for k=1: n Householder Transformations function [v]=house(x) v=x; v(1)=sign(x(1))*norm(x)+x(1); function [Q, R]=myqr(A) [m, n]=size(A); for k=1: n x=A(k: m, k) [v]=house(x); A(k: m, k: n)= A(k: m, k: n) –( 2/v’*v) v*(v’**A(k: m, k: n)); end