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"The de facto-standard library for linear algebra on the .NET platform is the Extreme Optimization Library."
- Jon Harrop, author, F# for Scientists
"I have yet to see another package that offers the depth of statistical analysis that Extreme Optimization does,
and I must say that I'm impressed with the level of service I've experienced."
- Henry Oh, RBC Capital Markets
"I have made it my mission to institutionalize the value of good
API design. I strongly believe that this is key to making developers
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- Brad Abrams,
Lead Program Manager,
Extreme Optimization Numerical Libraries for .NET
What's New in Version 4.2
- Automatic differentiation: symbolic computation of derivatives, gradients and Jacobians.
- Extensible with built-in support for derivatives of methods in System.Math and most elementary and special functions in the library.
- Backward differentiation with common sub-expression elimination generates optimal evaluation.
- New SymbolicMath class that lets you optimize functions and solve equations specified as lambda expressions using automatic differentation.
- New properties on optimizer and equation solver classes to support automatic differentation.
- New methods of the NonlinearCurve class to enable creation of a curve from a lambda expression using automatic differentation.
- Evaluation of (sequences of) classic orthogonal polynomials: Chebyshev (1st and 2nd kind), Hermite, Laguerre, Legendre and Gegenbauer.
- Stepwise linear regression.
- Regression fits of linearized curves: logarithmic, power, exponential, reciprocal...
- Chi-square test for proportions.
- 2x2 and RxC Contingency tables.
- Improved hypothesis test API.
- Extended methods and properties of logistic regression models.
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