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The Extreme Optimization Numerical Libraries for .NET are a collection of general-purpose mathematical and statistical classes built for the Microsoft .NET framework.

The new Version 3.0 adds support for multivariate analysis techniques like cluster analysis and principal component analysis (PCA), multivariate probability distributions, two-dimensional Fast Fourier Transforms (FFT's), and various smaller improvements.

The Extreme Optimization Numerical Libraries for .NET is the first complete platform for technical and statistical computing built on and for the Microsoft .NET platform version 2.0 and later. It combines a math library, a vector and matrix library, and a statistics library in one convenient package. A .NET 1.1 version is also available

At a glance:
Mathematics
Basic math: Complex numbers, solving equations in one or more variables, numerical integration, numerical differentiation, 'special functions'.
Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials.
Optimization: State of the art algorithms for finding the minimum or maximum of a function in one or more variables, linear programming.
Fast Fourier Transforms: 1D and 2D FFT's using 100% managed or fast native code (32 and 64 bit)
Vector and Matrix Library
Real and complex vectors and matrices.
Structured matrix types: including triangular, symmetrical and band matrices.
Sparse matrices.
Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition.
Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit).
Statistics
Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding.
Statistical Models: Simple, multiple, nonlinear and logistic regression. One and two-way ANOVA.
Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances.
Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions.
Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions.
Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers.
General features
Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical integration and differentiation, solving equations, complex numbers, and more.
Intuitive object model. The classes in the Extreme Optimization Numerical Libraries for .NET and the relationships between them match our every-day concepts.
Ground-breaking usability for numerical software development. The math itself is hard enough.
Great performance. We implemented the best algorithms available today to provide you with a robust, fast toolset.
Whether you develop applications in C#, Visual Basic .NET, Managed C++, or any of the other .NET Framework languages, the Extreme Optimization Numerical Libraries for .NET provides the reliable foundation and the building blocks developers need.

New in version 2.1:
Sparse Matrix Library. Efficiently calculate with huge, sparse matrices.
Linear Programming. Our dense LP solver is second to none.
Fast Fourier Transforms. Compute 1-dimensional FFT's using managed or native code.
New in version 2.0:
Matrix Debugger Visualizer. Inspect the elements of a matrix at debug time in table form. (screen shot)
Generic interfaces. For example, all collection classes support the appropriate IList interface.
New structured matrix types. Perform calculations on band matrices and diagonal matrices more efficiently.
Sort and filter data. New methods give you complete control of which observations are included in your statistical calculations.
Logistic Regression. Predict binary outcomes in terms of one or more variables.
Nonlinear Regression. An extension of our nonlinear curve fitting classes that gives you full access to the statistical properties of your model.