Improvements and Updates

What's New in IMSL

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What's New

With proven, tested algorithms and functions from Perforce IMSL, your team can call, embed and test an algorithm in a fraction of the time, saving your team a significant amount of development time and money.

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. Other enhancements include:

Additions

  • Added optional argument IMSL_MAX_ITERATIONS to set the maximum number of iterations.
  • Added functions to evaluate the pdf, cdf, and inverse cdf for the Weibull distribution.
  • Added function to perform sequential pattern mining using the PrefixSpan algorithm.
  • Added function to create a sequential database given a transaction database.
  • Added function to print a sequence database.
  • Added function to free the memory allocated for a sequence database.
     

Improvements

  • Corrected handling of datasets containing NaNs.
  • Added possibility to remove predictor variables by setting all entries in the column of a predictor variable to NaN.
  • Corrected matrix output for optional arguments IMSL_COEF_COVARIANCES and IMSL_COEF_COVARIANCES_USER in the case that the column dimension of the matrix is larger than the number of regression coefficients in the model (i.e., cov_col_dim > m, m the number of regression coefficients.)
  • Corrected output of optional arguments IMSLS_CRITERIONS, IMSLS_CRITERIONS_USER, IMSLS_INDEPENDENT_VARIABLES, IMSLS_INDEPENDENT_VARIABLES_USER, IMSLS_COEF_STATISTICS and IMSLS_COEF_STATISTICS_USER for the case of numerically singular input covariance matrix cov.
  • Corrected output printed via optional argument IMSLS_PRINT for the case of numerically singular input covariance matrix cov.
  • Corrected standard error output for the Bernoulli distribution (ipdf = 1) returned via optional arguments IMSLS_STD_ERRORS and IMSLS_STD_ERRORS_USER.
  • Corrected standard error output for the binomial distribution (ipdf = 2) returned via optional arguments IMSLS_STD_ERRORS and IMSLS_STD_ERRORS_USER.
  • Corrected standard error output for the negative binomial distribution (ipdf = 3) returned via optional arguments IMSLS_STD_ERRORS and IMSLS_STD_ERRORS_USER.
  • Corrected sign of Hessian for exponential distribution (ipf= 8) and normal distribution (ipf = 16) returned via optional arguments IMSLS_HESSIAN and IMSLS_HESSIAN_USER.
  • Corrected sign of (1,0) entry in the Hessian for the Pareto distribution (ipdf = 14) returned via optional arguments IMSLS_HESSIAN and IMSLS_HESSIAN_USER.
  • Corrected input values to ML optimization solver for log-logistic distribution (ipdf = 19).
  • Replaced approximate calculation of Hessian by analytical formulas for Weibull (ipdf = 10), extreme value (ipdf = 12), logistic (ipdf = 18) and log-logistic (ipdf = 19) distribution.
  • Activated computation of Hessian for Bernoulli (ipdf = 1), binomial (ipdf = 2) and negative binomial (ipdf= 3) distribution.
  • Replaced computation of inverse Hessian via LU factorization by the adjoint method.

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.