Developer Guide and Reference

  • 2021.2
  • 03/26/2021
  • Public Content

QR Decomposition

QR decomposition is a matrix factorization technique that decomposes a matrix into a product of an orthogonal matrix
and an upper triangular matrix
QR decomposition is used in solving linear inverse and least squares problems. It also serves as a basis for algorithms that find eigenvalues and eigenvectors.

Performance Considerations

To get the best overall performance of the QR decomposition, for input, output, and auxiliary data, use homogeneous numeric tables of the same type as specified in the
class template parameter.
Online Processing
QR decomposition in the online processing mode is at least as computationally complex as in the batch processing mode and has high memory requirements for storing auxiliary data between calls to the
method. On the other hand, the online version of QR decomposition may enable you to hide the latency of reading data from a slow data source. To do this, implement load prefetching of the next data block in parallel with the
method for the current block.
Online processing mostly benefits QR decomposition when the matrix Q is not required. In this case, memory requirements for storing auxiliary data goes down from LaTex Math image. to LaTex Math image..
Distributed Processing
Using QR decomposition in the distributed processing mode requires gathering local-node LaTex Math image. numeric tables on the master node. When the amount of local-node work is small, that is, when the local-node data set is small, the network data transfer may become a bottleneck. To avoid this situation, ensure that local nodes have a sufficient amount of work. For example, distribute the input data set across a smaller number of nodes.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at​.
Notice revision #20201201

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at