Hi John,

thanks for using Boost!

Boost.uBlas is a C++ template class library that primarily focuses on BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices, see https://www.boost.org/doc/libs/1_74_0/libs/numeric/ublas/doc/overview.html#functionality Recently, we also have introduced tensors.

Boost.uBlas is convient in terms familiarity and convenience if you have already experience with Boost and its setup. You will have most basic matrix operations such as matrix-matrix-multiplications. However, more complex linear algebra routines are not yet included.

Eigen is a framework itself and provides you with more functionality for now.

Good luck with your software project.


Am Mo., 30. Nov. 2020 um 18:38 Uhr schrieb John Mairs via ublas <ublas@lists.boost.org>:

Our software project has been using boost for years and we are very comfortable with it.  We have a new requirement to calculate eigenvalues from 6x6 matrix.  This is our first foray into linear algebra.  At first glance it appears boost ublas supports the 'vocabulary' types of matrix and vector and basic operations between them.  I also read about boost bindings between ublas and other popular linear algebra libraries.  

I took a python class on linear algebra and remember near the end of the course we had to write our own algorithm for computing eigen values so its probably in the realm of possible todo this ourselves using ublas only.  

Or we could use something non boost like eigen:

Any strong opinions folks?

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