# January 2020 Archives

## A Well Managed Household

Over the last few months we have seen how we can use a sequence of Householder transformations followed by a sequence of shifted Givens rotations to efficiently find the spectral decomposition of a symmetric real matrix M, formed from a matrix V and a diagonal matrix Λ satisfying

M × V = V × Λ

implying that the columns of V are the unit eigenvectors of M and their associated elements on the diagonal of Λ are their eigenvalues so that

V × VT = I

where I is the identity matrix, and therefore

M = V × Λ × VT

From a mathematical perspective the combination of Householder transformations and shifted Givens rotations is particularly appealing, converging on the spectral decomposition after relatively few matrix multiplications, but from an implementation perspective using ak.matrix multiplication operations is less than satisfactory since it wastefully creates new ak.matrix objects at each step and so in this post we shall start to see how we can do better.

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### Gallimaufry

 AKCalc ECMA Endarkenment Turning Sixteen

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