Developer Reference

  • 2022.1
  • 12/20/2021
  • Public Content
Contents

Least Squares and Eigenvalue Problems

The Intel® oneAPI Math Kernel Library includes LAPACK routines to solve least-squares problems, eigenvalue and singular value problems, and Sylvester equations.
To solve a particular problem, you can call two or more
computational routines
or call a corresponding
driver routine
that combines several tasks in one call, such as
?gesv
for factoring and solving. For example, to solve a system of linear equations with a general matrix, call
?getrf
(
LU
factorization) and then
?getrs
(computing the solution). Call
?gerfs
to refine the solution and get the error bounds. Alternatively, use the driver routine
?gesvx
, which performs all these tasks in one call.
You can also find an appropriate routine using the
characteristics
of your data and the
operations
you need by using the oneMKL LAPACK Function Finding Advisor, which helps you find the routine that suits your needs best.
The  standard LAPACK functions do not check the input data (matrices) for IEEE 754 floating point INFs or NANS. INFs and NANs will propagate through the computations and may cause unexpected results or instabilities. It is the user's responsibility to ensure that the input data do not contain INFs nor NaNs.
The LAPACKE (C interfaces to LAPACK) functions do check for NaNs in the input data (matrices) , and an error code is returned if a NAN is found.

Product and Performance Information

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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.