C / C++ Numerical Library

Embeddable Numerical Analysis Functions for C/C++ Applications

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.

"IMSL Numerical Libraries offer the most comprehensive, tested statistical functionality available, support major computing platforms, and were easily embeddable into the GlyphWorks Solution."
Jon Aldred, Product Manager, HBM-nCode
 
"By using IMSL Numerical Libraries, I can definitely say that 50% of my research time is save by simply calling functions like linear/nonlinear equation solvers and random number generators instead of coding and testing these subroutines myself."
Dr. Bhairavavajjula Nageswara Rao, Assistant Professor, IIT Madras
"Using rigorously tested algorithms from IMSL is clearly better than developing our own. Developers' time is extremely expensive in comparison to the cost of the libraries."
Principal, Research and Analytics Group, Major U.S. Bank
 
"With IMSL/Oracle combination, important statistical information is only a button click away. Even for complicated analysis of large data sets, the entire process is completed almost instantly."
Stephen J. Cottrell, Consultant, DuPont Pharmaceuticals
 
"The random number generator routines are the core of this model, and the IMSL Libraries are very fast and very accurate. We've had good results comparing simulations with real systems, enabling us to use the model in real time."
Dr. Giuseppe Brusasca, Research Scientist, ENEL
 

 

Typical Application Areas

Financial Services

  • Options pricing
  • Hedge fund trading analysis
  • Real-time, systematic risk management
  • Portfolio optimization

Retail

  • Content specific marketing
  • Just-in-time inventory
  • Touch point retail sales analytics
  • Risk assessment
  • Collection analytics
  • Computational biology analysis and modeling
  • ISVs embedding mathematical engines into their software offerings

Oil and Gas

  • Oil well performance analysis
  • Operations performance management

Consumer Products Goods

  • Real-time warranty & defect analysis
  • Supplier risk management
  • Resource hedging

Manufacturing

  • Resource planning
  • Production & maintenance analytics
  • Quality control

Functional areas included in the IMSL Numerical Libraries:

Data Mining and
Forecasting Functionality
Statistical
Functionality
Mathematical
Functionality
  • Decision Trees
  • Regression
  • Vector Auto-Regression/ Vector Error Correction Model
  • Apriori Analysis
  • Cluster Analysis
  • Kohonen Self Organizing Maps
  • Neural Networks
  • Auto_ARIMA
  • ARCH, GARCH
  • Support Vector Machines
  • Genetic Algorithms
  • Naïve Bayes
  • Logistic Regression
  • Principal Components Analysis
  • Factor Analysis
  • Discriminant Analysis
  • Bayesian Seasonal Model
  • Summary Statistics
  • Time Series and Forecasting
  • Nonparametric Tests
  • Analysis of Variance
  • Generalized Linear Models
  • Goodness of Fit
  • Distribution Functions
  • Random Number Generation
  • Hypothesis Testing
  • Design of Experiments
  • Visualization
  • Statistical Process Control
  • Multivariate Analysis
  • Correlations & Covariance
  • Optimization
  • Matrix Operations
  • Linear Algebra
  • Eigensystem Analysis
  • Interpolation and Approximation
  • Quadrature
  • Differential Equations
  • PDEs
  • Feynman-Kac Solver
  • Transforms
  • Nonlinear Equations
  • Linear and Non-Linear Programming
  • Special Functions
  • Utilities
             And many more

The IMSL C Numerical Library version 8.5 release added many sophisticated data mining algorithms.  The release added more data input streaming capability, as well as the ability to improve performance in multicore environments through the parallelization of various algorithms using OpenMP. The new release allows for 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, and Windows
  • Compilers including gcc, Microsoft, and Sun