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Our customers

This is a partial list of companies who are using our libraries:

ABB Robotics
Allstate
Arcam
Astra Schedule
Babson College
Canadian Council on Learning
Canyon Associates
Caxton Associates
CECity
Constellation Energy
CreditSights
DeepOcean
Duke University
Dynamotive
Elecsoft
Engelhard Corporation
Epcor
Equipoise Software
Galileo International
GAM UK
Gammex
GlaxoSmithKline
Global Matrix
The Hartford
Infinera Corporation
Intel
JDS Uniphase
LaBranche & Co.
Learning & Skills Council
Jacobs Consultancy
Litman Gregory
Lucas Systems
Malvern Instruments
Medrio
Merck & Co.
Mintera.
Monitor Software
MorningStar
NanoString Technologies
Paletta Invent
Parametric Portfolio Associates
Prosanos
RATA Associates
RiskShield
Ramboll
Standard & Poor's
Strategic Analysis Corporation
Univ. of Alicante
Univ. of South Carolina
vielife
Xerox
US Army

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Extreme Optimization Numerical Libraries for .NET

What's New in Version 4.2

Math Library

  • 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.

Statistics Library

  • 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.