Hi Gaurav,

thanks for your interest in uBLAS. You can find the documentation, examples and implementation of the tensor data type on Github.
Project 2 and 3 for the tensor extension on the Boost GSoC page are not defined yet.

A good starting point for implementing tensor algorithms are e.g. Tensor Decomposition or Fundamental Operations. The current data type supports conventional tensor contractions. However, we still might need some special products such as the hadamard or kronecker product in order to perform alternative least square algorithms, higher-order singular value decomposition or higher-order power methods for e.g. large-scale data analysis and quantum computing applications.

Best
Cem







Am Sa., 23. Feb. 2019 um 14:59 Uhr schrieb Gaurav Hoskote via ublas <ublas@lists.boost.org>:
I would like to contribute to ublas.tensor library. I am a final year engineering student. I have implemented a sample header file "vec.h" which uses Templated class for creation of vector and matrix as specified in the Programming Competency test section of GSOC 2019 ideas. I have used operator overloading to carry out operations like matrix multiplication, addition, subtraction etc.
The output and time taken for each operation is printed in "output.txt" and the code in "main.cpp". 
Having read the code of headers provided in the link, I am finding it a little confusing writing my proposal as to which type of Tensor algorithms are needed. Can someone please mentor/elaborate and also give an example of an algorithm which can be implemented.
You can see my code here: https://github.com/gauravhoskote/codes/tree/master/gsoc2019/boost/numeric/ublas/tensor/competencytest
Regards,
Gaurav Hoskote.
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