Getting Started Guide

Contents

Details

Given the matrix
X
of size
n
x
p
, the problem is to compute the Singular Value Decomposition (SVD)
X
=
U
Σ
V
t
,
where
  • U
    is an orthogonal matrix of size
    n
    x
    n
  • Σ
    is a rectangular diagonal matrix of size
    n
    x
    p
    with non-negative values on the diagonal, called singular values
  • V
    t
    is an orthogonal matrix of size
    p
    x
    p
Columns of the matrices
U
and
V
are called left and right singular vectors, respectively.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804