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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

Extreme Optimization Numerical Libraries for .NET

What's New in Version 5.1

Math library

• Support for quadruple precision numbers with up to 34 digits, including our full vector and matrix library. Real and complex numbers are supported.
• Smoothing cubic splines, monotonicity preserving splines.
• Nonlinear curve fitting for arbitrary domains, including surface fitting.
• Improved Quadratic Programming solver.

Vector and Matrix Library

• GPU computing: offload computations to the GPU.
• Data is kept on the GPU as long as possible for optimal performance.
• Row and column labels for vectors and matrices.
• Automatic alignment on labels.
• Grouping and aggregation on vectors and matrices.
• Optimized elementary functions on vectors and matrices.
• Reshaping operations on vectors and matrices.
• Support for F# 3.1 indexing and slicing.

Data Frame Library

• Data frame class supporting advanced data manipulation.
• Simple and multi-level indexes.
• Pivot tables, stacking, unstacking of data frames.
• Joins and merges, join to nearest.
• Advanced grouping and aggregation.
• Supports LINQ queries.
• Integrated with statistical models.

Statistics Library

• GARCH models (Generalized AutoRegressive Conditional Heteroskedasticity).
• Regularized linear regression models: Ridge regression, LASSO and elastic net.
• Cumulative multivariate normal distribution.
• Hyperbolic and Inverse Gaussian distribution.
• Generic truncated distributions.
• Post hoc tests for one-way ANOVA: Tukey, Tukey-Kramer, Scheffé, Fisher-Hayter
• Ljung-Box test for autoregression.