Generic QR factorization #9
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added 3 commits
May 11, 2014 20:05
This allows the implementation of householder reflection without relying on knowledge of DVec. This required a new member in the Indexable trait: the shape() function, which returns the maximum index available.
But static matrices can't use it yet, they need to implement the Row/Col slicing traits.
The ColSlice implementation for fixed size matrices returns a DVec, while this is probably not optimal performance-wise, the dynamic nature of the result makes this necessary. Using a data type presenting the ImmutableVector trait would solve this, but it looks like a non-trivial change.
| let offset2 = self.offset(row2, col2); | ||
| let count = self.mij.len(); | ||
| assert!(offset1 < count); | ||
| assert!(offset1 < count); |
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Duplicated line. I guess this should be offset2 < count?
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Hi, this is a very good work!
This would allow the user to write |
added 6 commits
May 12, 2014 21:54
Also removed a bunch of duplicate trait usages
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Hi, I've updated my branch taking your remarks into account, and updated the code to the latest rust in the meanwhile. It is now required to implement the trait FloatMath in order to use math methods such as sin() or exp(). In the process of converting to FloatMath where necessary I've also removed some overspecified traits (like Num+Float) |
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Thanks! |
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Hi,
Please find here a proposed implementation of a generic QR factorization. It relies on extending the Indexable trait so that it is implemented by all data types, and implements the ColSlice and RowSlice traits for static sized matrices by returning a DVec. For now I can't find a better way to implement this trait.