[eigen] Matrix decompositions |

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*To*: eigen <eigen@xxxxxxxxxxxxxxxxxxx>*Subject*: [eigen] Matrix decompositions*From*: Sameer Agarwal <sameeragarwal@xxxxxxxxxx>*Date*: Thu, 28 Feb 2013 11:28:54 -0800*Dkim-signature*: v=1; a=rsa-sha256; c=relaxed/relaxed; d=google.com; s=20120113; h=mime-version:x-received:date:message-id:subject:from:to :content-type; bh=Zd+OArjzIviSR76dCeRQjNRXelmKqovIVHWALF1x1Gc=; b=SayX0k2XcCdTlBOInWqBeURf1zeYcNEFTHU09BGoenQnGKmzARdQP3gZahZ6kLWpHI t7POyiDN9jsAmC+BlgxMOisuXwptEIceasargGAqqPXc2rhgxQYUMMdm0+bIdVAd0kpa 3vmTdR6KpacCeaT4/lvc8QM6OrPme0awkgiEX3RU/NcjfssoN1YqNdyGH4WQ7Ab3wDVE iQqxGghc3s4OUL3ErOBRzQEzp4492XSSrDPv/dU2VHr78ztGFSPhQruwFGUjJy6EBqqb A96g+F/dGffARKbxlQrbNarl1XqhX+pKdagM1T22RPpWwygPQYDwAGL4lesfaw41hlEc H+8Q==

Hi Guys,

Admittedly I can run benchmarks for this sort of thing, but I think an authoritative answer from the authors is better.

1. What if any impact does the row/column major storage have on the various matrix decomposition algorithms in Eigen? I imagine, that QR factorization for example will do better if the storage is column major.

2. Are the matrix decomposition algorithms affected by the alignment of the underlying storage, or more generally, do the matrix decompositions use SIMD vectorization which in turns require alignment of the underlying data?

In this case I did do some benchmarking, where I used an eigen Matrix and a block of memory allocated using new and wrapped inside a Map object, and performed cholesky factorization on numerically identical (row major matrices) and did not notice any difference. Thus the curiosity. It is entirely possible that my benchmark is broken.

Thanks,

Sameer

**Follow-Ups**:**Re: [eigen] Matrix decompositions***From:*Gael Guennebaud

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