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SVD behavior in mp different from Matlab when applied to negative scalar

8 лет назад • обновлен 8 лет назад • 2

I have the following question:

In one of my examples I need to compute the Singular Value Decomposition (SVD) of a negative scalar, specifically -0.7276.

In double precision, Matlab returns:

>> [U S V]=svd(-0.7276)

U = -1

S = 0.7276

V =1

In MP, I obtain:

s=mp('-0,7276');

>> [U,S,V]=svd(s)

U = 1

S = -0.7276

V = 1

The definition of SVD states that the matrix S should have nonnegative diagonal entries. In the above example, the first two elements seem to be multiplied by -1 compared to Matlab output. While the product doesn't change, the unexpected negative sign of the singular value causes problems in subsequent code.

Is this a bug or is there an option to make it return the same output as Matlab's svd?

The 4.2.3.11967 release of the toolbox was used.

Thank you,

Denis

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