IMSL® C Numerical Library

Embeddable Numerical Analysis Functions for C/C++ Applications

IMSL C Library

The IMSL C Numerical Library provides advanced mathematical and statistical functionality for programmers to embed in their existing or new applications. Written in standard C, the IMSL C Library can be embedded into any C or C++ application as well as any existing application that can reference a C library.

Using PyIMSL, developers also have the option to write programs in Python that leverage algorithms in the IMSL C Library. PyIMSL is a collection of Python wrappers to the mathematics and statistical algorithms in the IMSL C Library and is available either as a separate download or as part of the PyIMSL Studio product.

Typical Application Areas

  • Portfolio optimization in financial services
  • Risk management in financial services
  • Inventory management and demand forecasting
  • Modeling and simulation in high performance computing
  • Computational biology analysis and modeling
  • ISVs embedding mathematical engines into their software offerings

Functional areas included in the IMSL Numerical Libraries:

Mathematics

Statistics

  • Matrix Operations
  • Linear Algebra
  • Eigensystems
  • Interpolation & Approximation
  • Quadrature
  • Differential Equations
  • Feynman-Kac Solver
  • Transforms
  • Nonlinear Equations
  • Optimization
  • Special Functions
  • Utilities
  • Basic Statistics
  • Time Series & Forecasting
  • Nonparametric Tests
  • Correlation & Covariance
  • Data Mining
  • Regression
  • Analysis of Variance
  • Survival Analysis
  • Density and Hazard Estimation
  • Goodness of Fit
  • Distribution Functions
  • Random Number Generation

 

The IMSL C Numerical Library version 8.0 release provides improved performance in multi-core environments through the parallelization of numerous algorithms using OpenMP and greater depth of functionality via new and updated algorithms. In addition to these enhancements , the IMSL C Library is tuned and validated for compatibility, numerical accuracy, and performance on widely adopted platforms, including:

  • Common 32-bit and 64-bit microprocessor architectures
  • Operating Systems including Linux, Unix, Windows
  • Compilers including gcc, Microsoft and Sun