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GSoC-2021 : Matrix State of the Art

I was selected for GSoC 2021 under the organization Pharo Consortium to work on the project, Matrix State of the Art.

Brief description of the project

Today, many fields of computational sciences (Data Science, Data Visualization and even Machine Learning), mathematics, engineering, geology and others make use of matrices. Usually described from tables, it is a central tool used by many programming languages.

A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array. We are inspired by historical sources such as APL and NumPy. In the PolyMath project (a Pharo project that implements numerous mathematical algorithms), there is already a support for vectors (tensors with rank 1) and matrices (tensors with rank 2), but not general multi-dimensional matrices (aka tensors). The main objective of this project is to extend the existing PolyMath classes to support tensors and related operations.

Implementations carried out

Additional work to be carried out

It would also be important to generalize operations such as outer product and the kronecker product for matrices of dimension greater than 2

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