Developer Reference

Intel® oneAPI Math Kernel Library LAPACK Examples

ID 766877
Date 12/20/2021

A newer version of this document is available. Customers should click here to go to the newest version.

Document Table of 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.