Het ontbinden van een matrix m.b.v. een getraind neuraal netwerk

Bachelor thesis (2020)

Authors

S.R. Veldkamp Electrical Engineering, Mathematics and Computer Science

Contributors

M. Möller Numerical Analysis - EEMCS (mentor)
Y. van Gennip Mathematical Physics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Susan Veldkamp
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Published Date

24-07-2020

Language

Dutch

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Before making matrix calculations, it can be useful to decompose these matrices in some way, such as according to an LU-decomposition. This can save a lot of computing power and thus time. Several computer programs exist that can decompose matrices in multiple ways. However, this report answers the question of whether it is also possible to make these decompositions in another way: namely with the help of a trained neural network. In this report is described how a trained neural network can help in finding LU decompositions of 2x2 matrices.
By making good use of the structure of a neural network, and by choosing a fitting optimizer, learning rate and loss function, a neural network can indeed quickly learn to make a LU-decomposition of a matrix. With this, it is readily shown that classical algorithms such as LU decompositions can be reproduced with trained neural networks.

Files

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