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Optimal orthogonalization processes
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作者 Marko Huhtanen Pauliina Uusitalo 《Science China Mathematics》 SCIE CSCD 2022年第1期203-220,共18页
Two optimal orthogonalization processes are devised toorthogonalize,possibly approximately,the columns of a very large and possiblysparse matrix A∈C^(n×k).Algorithmically the aim is,at each step,to optimallydecr... Two optimal orthogonalization processes are devised toorthogonalize,possibly approximately,the columns of a very large and possiblysparse matrix A∈C^(n×k).Algorithmically the aim is,at each step,to optimallydecrease nonorthogonality of all the columns of A.One process relies on using translated small rank corrections.Another is a polynomial orthogonalization process forperforming the Löwdin orthogonalization.The steps rely on using iterative methods combined,preferably,with preconditioning which can have a dramatic effect on how fast thenonorthogonality decreases.The speed of orthogonalization depends on howbunched the singular values of A are,modulo the number of steps taken.These methods put the steps of the Gram-Schmidt orthogonalizationprocess into perspective regardingtheir(lack of)optimality.The constructions are entirely operatortheoretic and can be extended to infinite dimensional Hilbert spaces. 展开更多
关键词 optimal orthogonalization sparse matrix Gram-Schmidt orthogonalization lowdin orthogonalization polynomial orthogonalization implicit orthogonalization PRECONDITIONING Gram matrix frame inequality
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