QR decomposition

QR Decompositions and Rank Deficient Matrices

We discuss the necessary changes to our QR decomposition algorithms to handle matrices which do not have full rank.

QR Comparison with other Implementations

We developed a QR decomposition algorithm, based on the orthogonalisation process of Gram-Schmidt in a series of posts here, here, here, and here. Let’s have a look how good this algorithm performs against built-in implementations from julia and other programming languages.

QR Decompositions with Reorthogonalisation

Problem Formulation We already discussed QR decompositions and showed that using the modified formulation of Gram-Schmidt significantly improves the accuracy of the results. However, there is still an error of about $10^3 M_\varepsilon$ (where $M_\varepsilon$ is the machine epsilon) when using the modified Gram Schmidt as base algorithm for the orthogonalisation.

QR Decompositions

We consider the necessary changes to the Gram-Schmidt orthogonalisation to obtain a QR Decomposition

Floating Point Accuracy and Precision

Floating point computations on computers may behave differently than one might expect. Every software developer should be aware of these since computed results may be off by orders of magnitude in the worst case.