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A greedy algorithm for the optimal basis problemPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 1997 (English)In: BIT Numerical Mathematics, ISSN 0006-3835, E-ISSN 1572-9125, Vol. 37, no 3, 591-599 p.Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

Springer, 1997. Vol. 37, no 3, 591-599 p.
##### Keyword [en]

Optimal basis problem; the Hadamard condition number; minimum spanning tree problem; greedy algorithm; secant approximation of derivatives; interpolation methods
##### National Category

Computational Mathematics
##### Identifiers

URN: urn:nbn:se:liu:diva-78866DOI: 10.1007/BF02510241OAI: oai:DiVA.org:liu-78866DiVA: diva2:536299
#####

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Available from: 2012-06-21 Created: 2012-06-21 Last updated: 2015-06-02

The following problem is considered. Given *m*+1 points {*x* _{i }}_{0} ^{m }in *R* ^{n }which generate an *m*-dimensional linear manifold, construct for this manifold a maximally linearly independent basis that consists of vectors of the form *x* _{i }−*x* _{j }. This problem is present in, e.g., stable variants of the secant and interpolation methods, where it is required to approximate the Jacobian matrix *f*′ of a nonlinear mapping*f* by using values of*f* computed at *m*+1 points. In this case, it is also desirable to have a combination of finite differences with maximal linear independence. As a natural measure of linear independence, we consider the hadamard condition number which is minimized to find an optimal combination of *m* pairs {*x* _{i },*x* _{j }}. We show that the problem is not NP-hard, but can be reduced to the minimum spanning tree problem, which is solved by the greedy algorithm in *O*(*m* ^{2}) time. The complexity of this reduction is equivalent to one *m×n* matrix-matrix multiplication, and according to the Coppersmith-Winograd estimate, is below *O*(*n* ^{2.376}) for *m=n*. Applications of the algorithm to interpolation methods are discussed.

References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1080",{id:"formSmash:lower:j_idt1080",widgetVar:"widget_formSmash_lower_j_idt1080",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1081_j_idt1083",{id:"formSmash:lower:j_idt1081:j_idt1083",widgetVar:"widget_formSmash_lower_j_idt1081_j_idt1083",target:"formSmash:lower:j_idt1081:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});