Roguewave – IMSL Numerical Libraries



IMSL Numerical Libraries

Battle-tested algorithms from prototype to production

Analyzing data has never been more important — or harder. Getting actionable information from your large and disparate datasets can often determine if your organization meets its goals. Create competitive differentiation and unlock innovation by using the most trusted, tested, and reliable algorithms available. Backed by a team of mathematicians and statisticians, IMSL® Numerical Libraries allow you to address complex problems quickly with a variety of readily-available algorithms. With IMSL you get consistency from prototype to production.

The largest collection of commercially-available mathematical and statistical functions for data mining and analysis, IMSL embeddable algorithms are used in a broad range of applications across all industries. Organizations in finance, telecommunications, oil and gas, government, aerospace, and manufacturing depend on the robust and portable IMSL Libraries to efficiently build high-performance, mission-critical applications, including applications used to enable the innovative study of the human genome. Create, innovate, and implement technology to meet your strategic objectives.

Embeddable mathematical and statistical functionality

IMSL Libraries save development time by providing optimized mathematical and statistical algorithms that can be embedded into C, C++, .NET, Java™, and Fortran applications, including many databases. IMSL enhances application performance, reliability, portability, scalability, and maintainability as well as developer productivity. IMSL Libraries are supported across a wide range of languages as well as hardware and operating system environments including Windows, Linux, Apple, and many UNIX platforms.

Functional areas in the IMSL Numerical Libraries:

Data mining and forecasting functionality Statistical functionality Mathematical functionality
Stochastic Gradient Boosting Summary Statistics Optimization
Decision Trees Time Series and Forecasting Matrix Operations
Regression Nonparametric Tests Linear Algebra
Vector Auto-Regression/Vector

Error Correction Model

Analysis of Variance Eigensystem Analysis
Apriori Analysis Generalized Linear Models Interpolation and Approximation
Cluster Analysis Goodness of Fit Quadrature
Kohonen Self Organizing Maps Distribution Functions Differential Equations
Neural Networks Random Number Generation PDEs
Auto_ARIMA Hypothesis Testing Feynman-Kac Solver
ARCH, GARCH Design of Experiments Transforms
Support Vector Machines Visualization Nonlinear Equations
Genetic Algorithms Statistical Process Control Linear and Nonlinear Programming
including Sparse LP Solver
Naïve Bayes Multivariate Analysis Special Functions
Logistic Regression Correlations and Covariance Utilities
Principal Components Analysis
Factor Analysis
Discriminant Analysis
Bayesian Seasonal Model



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