What's New in IMSL

Highlight improvements in IMSL C 2026.1.0 include support for popular machine learning platforms, including oneAPI 2025.3, Visual Studio 2026, and RHEL 10. 

In addition to platform work for this release, it also includes:

Probability Distribution Functions and Inverses

• weibull_pdf, weibull_cdf, weibull_inverse_cdf Evaluates the pdf, cdf, and inverse cdf for the Weibull distribution. The Weibull distribution is widely used in reliability engineering, materials science, weather modeling, quality control, and finance.

Data Mining

• prefix_span Performs sequential pattern mining using the PrefixSpan algorithm. Sequential Patterns are frequently occurring sequences of items. Such items could be products, genes, behaviors, symptoms of disease, virtually any observable, discrete event. Applications for SPM include problems in medicine, bio-informatics, economics, psychiatry, retail and e-commerce, and many others. The PrefixSpan algorithm is a depth-first search that "discovers" or grows sequential patterns by way of projection and recursion.

The full list of the latest supported platforms is available online.

Change Log 

IMSL For Fortran 2025.1

IMSL (R) Fortran Numerical Library provides the essential building blocks needed to develop analytic applications for your organization. Recent updates to IMSL (R) Fortran 2025.1 provide new features and bug fixes, including added nonsingularity check for the U factor in array FACT in the LAPACK case. 

IMSL (R) Fortran 2025.1 also includes support for the GNU Fortran compiler on Red Hat Enterprise Linux 9 and the Intel oneAPI 2025 compiler.  

The full list of the latest supported platforms is available online.

IMSL For Fortran (FNL) 2024.1

The latest release of FNL includes several platform updates including RHEL 9, Intel oneAPI 2024.0 and support for the new oneAPI IFX compiler on several platforms. 

The full list of the latest supported platforms is available online.

IMSL C 2025.1

IMSL C Numerical Library provides the essential building blocks needed to develop analytic applications for your organization. Recent updates to IMSL C 2025.1 provide new features and bug fixes, including three functions for negative binomial distribution. The negative binomial distribution provides a useful model for counts of discrete random events, such as incidences of disease in certain populations, occurrences of tornados across the country, and many others.  

In addition, there is now a graph-based variant of the simplex method normally used to solve the transportation problem. By finding an initial solution relatively close to the optimum, this method often results in fewer steps being required to get the optimal solution. Internal experimentation has shown this method to be often significantly faster than the two other methods available, the revised simplex and the interior-point method. 

Additional features include: 

• Support for Intel oneAPI 2025
• Performance improvements and a new optional argument to control tolerance in internal rate of return
• Functions to calculate the determinant of real or complex square matrices 

For more information, see the release notes. 

Release Notes 

IMSL C 2024.1

New Features

• Developers are now able to generate, store, and reuse gradient boosted models in IMSL.  This allows the developer to work in two phases to first generate a model and then use that model in a production environment without having to train the model each time.
• IMSL now allows you to compute eigenvalues and eigenvectors of symmetric and Hermitian band matrices in band symmetric storage format.

Bug Fixes

• This release includes several bug fixes and improvements in memory use and error handling.  Updating to this version will help you stay up to date with underlying platforms and provide the most stable environment for your analytics applications.
• The full list of fixes is available in the readme.