# Singular Value Decomposition

Singular Value Decomposition (SVD) is one of matrix factorization
techniques. It has a broad range of applications including
dimensionality reduction, solving linear inverse problems, and data
fitting.

## Details

Given the matrix

*of size , the problem is to compute the Singular Value Decomposition (SVD) , where:*X

is an orthogonal matrix of sizeU is a rectangular diagonal matrix of size with non-negative values on the diagonal, called singular values is an orthogonal matrix of size

Columns of the matrices

*and*U

*are called left and right singular vectors, respectively.*V

## Computation

The following computation modes are available:

## Examples

C++ (CPU)

Batch Processing:

Online Processing:

Distributed Processing:

Java*

There is no support for Java on GPU.

Batch Processing:

Online Processing:

Distributed Processing:

Python*

## Performance Considerations

To get the best overall performance of singular value decomposition
(SVD), for input, output, and auxiliary data, use homogeneous numeric
tables of the same type as specified in the algorithmFPType class
template parameter.

Online Processing

SVD 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
compute() method. On the other hand, the online version of SVD 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 compute() method for the current block.

Online processing mostly benefits SVD when the matrix of left
singular vectors is not required. In this case, memory
requirements for storing auxiliary data goes down from
to
.

Distributed Processing

Using SVD in the distributed processing mode requires gathering local-node
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 input data set across a smaller number of nodes.

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