Advanced Numerical Analysis
As the only numerical library of its kind to offer unprecedented analytic capabilities and charting, the IMSL .NET / C# Numerical Library can be referenced from any .NET Framework language including C#, F# and Visual Basic .NET. Version 6.5 of the IMSL .NET / C# Numerical Libraries provides the most comprehensive, high-performing and accessible mathematical, statistical and financial algorithms for the .NET Framework and Microsoft Silverlight.
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Visual Studio 2010 Solution Case Study
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Achieve Performance Increases with New Parallel Computing Capabilities
With .NET Framework 4 and Visual Studio™ 2010, Microsoft has extended the threading capabilities of the .NET Framework with the Task Parallel Library. The IMSL .NET / C# Numerical Library has integrated these threading patterns into dozens of functions, resulting in easy access to parallel-processing performance increases that take advantage of multi-core hardware.
Save Development Effort by Embedding IMSL .NET / C# Functions
The algorithms available within the IMSL .NET / C# Numerical Library cover all of the major categories of functionality commonly used in numerical analysis, from commonly used math and statistical analysis functions like optimization and regression to advanced neural network and classification technology. This math and statistical algorithm functionality can be applied to an unlimited set of applications, such as bioinformatics and life sciences, fraud detection, risk management and portfolio optimization, manufacturing yield analysis and more.
Functional areas included in the IMSL .NET / C# Numerical Library:
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Statistics |
- Matrix Operations
- Linear Algebra
- Eigensystems
- Interpolation & Approximation
- Numerical Quadrature
- Differential Equations
- Nonlinear Equations
- Optimization
- Special Functions
- Finance & Bond Calculations
- Genetic Algorithm
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- Basic Statistics
- Time Series & Forecasting
- Nonparametric Tests
- Correlation & Covariance
- Data Mining
- Regression
- Analysis of Variance
- Transforms
- Goodness of Fit
- Distribution Functions
- Random Number Generation
- Neural Networks
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