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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

New Version 6.0!

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Extreme Optimization Numerical Libraries for .NET

What's New in Version 6.0

Universal improvements

  • There now is one setup for both 32 and 64 bit operating systems.
  • Libraries can also be installed as NuGet packages, for easier upgrading.
  • The documentation has been fully updated to the latest API.
  • New and updated QuickStart samples illustrate the new API.

Math Library

  • Complex numbers are now generic over the type of the real and imaginary parts.
  • Flexible genetic optimization framework.
  • Dormand-Prince adaptive RK ODE solver
  • Akima splines and cubic Hermite splines.
  • Evaluation of orthogonal polynomials.
  • Error functions for Quad precision numbers.
  • Vector functions for complex single and double precision arguments.
  • Faddeeva function, Voigt function, complex error function.
  • Savitsky-Golay and Moving Average smoothing in SignalProcessing.

Data Analysis Library

  • Support for LINQ queries on data frames, vectors and matrices.
  • New groupings for aggregations include: fixed and expanding windows, partitions, groupings on value and quantiles, 2D pivot tables, and resampling.
  • A large set of aggregators and aggregator functions has been added with efficient implementations for specific types of groupings (e.g. moving averages).
  • New generic Descriptives class for collecting descriptive statistics of vectors.
  • Indexes on ordered types now support lookup nearest. Likewise, data frames with such indexes now support join on nearest.
  • New Recurrence type lets you specify date/time patterns for use in, for example, resampling of data frames.

Vector and Matrix Library

  • All vector and matrix classes are now generic, including sparse matrices and complex versions.
  • New static Vector and Matrix classes remove the need to specify the element type as a generic type argument.
  • New mutability options now let you create read-only vectors, and writable vectors with copy-on-write semantics.
  • Most operations on vectors and matrices are available in three forms: as a static method that returns a new value, as a static method that returns the value in a supplied object, and as an instance method that modifies the object in-place.
  • Operator methods are named consistently for static calls, in-place and out-of-place updates.
  • Relational operators are now supported for generic matrices.
  • The native libraries have been upgraded to Intel MKL version 11.3 Update 2.
  • The native libraries now support Conditional Numerical Reproducibility.
  • The CUDA libraries for 64 bit have been upgraded to CUDA version 7.5.
  • A fully managed implementation of the linear algebra library for single-precision floating-point numbers was added.

Statistics Library

  • Descriptions tailored to working in an interactive environment for statistical models, hypothesis tests and other objects.
  • Statistical models have been integrated with the DataFrame library.
  • The variable types (NumericalVariable, CategoricalVariable, DateTimeVariable) have been replaced with generic vectors.
  • Categorical variables are expanded into indicator variables when necessary. Several encoding schemes are available.
  • Models can be persisted in a form suitable for deployment in predictive modeling applications.
  • Models can be specified using R-style formulas.
  • Regression models are now robust against collinearity.
  • New distribution classes have been added: truncated distributions, inverse Weibull.