Computing Truncated Joint Approximate Eigenbases for Model Order Reduction

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Publication date: 15 January 2022

Abstract: In this document, some elements of the theory and algorithmics corresponding to the existence and computability of approximate joint eigenpairs for finite collections of matrices with applications to model order reduction, are presented. More specifically, given a finite collection

X_{1}, l d o t s, X_{d}

of Hermitian matrices in

m a t h b b C^{n i m e s n}

, a positive integer

r l l n

, and a collection of complex numbers

h a t x_{j, k} i n m a t h b b C

for

1 l e q j l e q d

,

1 l e q k l e q r

. First, we study the computability of a set of

r

vectors

w_{1}, l d o t s, w_{r} i n m a t h b b C^{n}

, such that

w_{k} = a r g m i n_{w i n m a t h b b C^{n}} s u m_{j = 1}^{d} | X_{j} w - h a t x_{j, k} w |^{2}

for each

1 l e q k l e q r

, then we present a model order reduction procedure based on the truncated joint approximate eigenbases computed with the aforementioned techniques. Some prototypical algorithms together with some numerical examples are presented as well.

Has companion code repository: https://github.com/fredyvides/pytjae

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