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Extreme Optimization Numerical Libraries for .NET
Below is a non-exhaustive list of the major features that were added in recent upgrades.
Smaller improvements and additions are not in this list.
New in version 4.2 (December, 2011)
- Automatic differentiation
- Supply functions and constraints as lambda expressions and have gradients computed automatically.
- Backward differentiation with common sub-expression elimination for optimal performance.
- Extensible with built-in support for methods of System.Math and most elementary and special functions in the library.
- Orthogonal polynomials Chebyshev, Gegenbauer, Hermite, Laguerre, Legendre polynomials and sequences.
- Stepwise regression Automatic variable selection for linear regression.
- Contingency tables Both 2x2 and RxC tables are supported.
- Improved SQL Server support Easier deployment.
New in version 4.1 (July, 2011)
- Optimization framework. Provides a generic model for defining and solving optimization problems.
- Quadratic Programming. Solve quadratic optimization models with linear constraints.
- Nonlinear Programming. Optimize nonlinear functions with linear or nonlinear constraints.
- New Decimal functions extend all the functions in System.Math to the decimal type, including sin, cos, exp.
- Improved elementary functions. Evaluate sine, cosine and tangent accurately for huge arguments.
- Iterative sparse solvers Efficiently solve systems with many thousands of variables, optionally using preconditioners.
- New probability distributions LogSeries and Maxwell.
New in version 4.0 (November, 2010)
- Full .NET 4.0 Support. Including samples and projects for Visual Studio 2010.
- Full F# 2.0 Support. Including more than 50 new samples.
- Multi-core Ready. Many algorithms have been parallelized using the .NET Task Parallel Library.
- Improved sparse solvers using fill-reducing column orderings.
- New Sparse Linear Program Solver can solve problems with more than 1 million variables.
- Mixed integer programming.
- New Special functions. Hypergeometric, elliptic integrals, Fresnel, Riemann zeta.
- FFT Window Functions.
New in version 3.6 (February, 2010)
- Single-preicision vector and matrix library
- Non-negative Matrix Factorization (NMF)
- Non-negative Least Squares (NNLS)
- Numerical integration in 3 or more dimensions
New in version 3.5 (October 2009)
- Full .NET 3.5 Support. Including samples and projects for
Visual Studio 2008.
- Ordinary Differential Equations. Integrate stiff and non-stiff
systems of Ordinary Differential Equations (ODE's) using our state-of-the art
- Improved Curve Fitting. We made our algorithms more robust, and added
new features, including confidence and prediction bounds.
- Time Series Models. Exponential smoothing and ARIMA
(Auto-Regressive Integrated Moving Average) models.
New in version 3.1:
- Arbitrary precision numbers. BigInteger, BigRational, BigFloat.
- Generic Arithmetic Framework. Write algorithms that can use any
- Generic Linear Algebra. A complete generic vector and matrix
- 2D Numerical Integration.
- Generalized Linear Models. Poisson regression, probit regression and
New in version 3.0:
- 2D Fast Fourier Transforms. Compute 2-dimensional FFT's using managed
or native code.
- Multivariate Statistical Analysis. Principal Component Analysis
(PCA), K-means Clustering, Hierarchical Clustering.
- Multivariate Probability Distributions. Multivariate normal and
New in version 2.1:
- Sparse Matrix Library. Efficiently calculate with huge, sparse
- 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<T> 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
- Nonlinear Regression. An extension of our nonlinear curve fitting
classes that gives you full access to the statistical properties of your model.
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