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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 7.0

.NET Core and .NET Standard support

• Support for .NET Core 1.1 and 2.1.
• Support for .NET Standard 1.3 and 2.0.
• Support for .NET Framework 3.5, 4.0, 4.72 and later.
• All packages are available on the Nuget Gallery.

Linear algebra

Major enhancements

• Broadcasting vectors in matrix operations.
• Enable Conditional Numerical Reproducibility option for native libraries.
• Upgraded native libraries to Intel® Math Kernel Library version 2019 Update 0.
• Upgraded managed linear algebra library to LAPACK 3.7.0.
• Improved range and accuracy of matrix exponential.
• Vector Map methods that include index as delegate argument.

New matrix decompositions

• Generalized Eigenvalue Decomposition.
• Generalized Singular Value Decomposition (GSVD).
• Sparse singular value decomposition.
• RQ decomposition, QL decomposition, and LQ decomposition.
• Access to 'thin' version of the orthogonal factor Q in a QR decomposition.
• Compute factors of symmetric and Hermitian indefinite decomposition.

Performance improvements

• Improve performance for level 2 managed sparse BLAS.
• Improve performance for various vector operations.
• The threshold for parallel execution of vector maps can now be configured.

Mathematics

General improvements

• The generic Operations<T> class has been optimized to eliminate nearly all overhead for the most frequently used operations on the most common argument types.
• ParallelOptions is now exposed for all algorithms to enable cancellation and other scenarios.
• Combinatorial iterators to enumerate all combinations, permutations, and Cartesian products of sets of items.
• New overloads for numerical integration methods that take Interval objects to specify bounds.
• Inverse hyperbolic functions for decimal and quad precision numbers.

Optimization

• The NonlinearProgram class has a new constructor that accepts variable names.
• Symbolic constraints that are linear in the variables are now recognized as such.
• The Nonlinear Program solver can now recover when it encounters an infeasible subproblem.
• Up to 30% improvement in the performance of the Linear Program solver
• Limited Memory BFGS Optimizer.
• LeastSquaresOptimizer base class for nonlinear least squares algorithms.
• Trust Region Reflexive algorithm for nonlinear least squares.
• Trust Region Reflexive algorithm option in nonlinear curve fitting.
• Improved documentation for nonlinear least squares algorithms.

Special functions

• Jacobi elliptic functions.
• Zeros of Bessel and Airy functions.
• The performance and accuracy of Bessel functions of the first and second kind has been improved.
• Polygamma function.
• Modified Bessel functions of real order.
• "Partial application" functions for incomplete and regularized Gamma and Beta functions.
• Zernike polynomials.

Statistics and data analysis

Data access library

• Data Access Library providing a unified API for reading and writing data frames, matrices, and vectors.
• Reading and writing R's .rda/.rdata and .rds files.
• JSON serialization.
• Other supported formats include: delimited text (CSV, TSV...), fixed-width text, Matrix Market, Matlab®, stata®

Statistical models

• Use R-style model formulas to specify statistical models.
• Partial Least Squares (PLS) models.
• Linear Discriminant Analysis.
• Kernel Density Estimation.
• Binomial Generalized Linear Model can now be used with count data.
• Two-way ANOVA: support for Type I, Type II, and Type III sums of squares.
• New ConditionalVariances property on GARCH models.
• The performance of ARIMA model fitting has been improved.
• Nicer Summarize for statistical models.

Hypothesis tests

• Augmented Dickey-Fuller test.
• Cramer-von Mises Goodness-of-fit test.
• Tests for outliers: Grubbs' test, Generalized ESD test.

Data analysis

• New aggregators: Range, Mode, CountUnique.
• Improved support for custom aggregators based on accumulators.
• R-style variations of quantiles.
• LOESS and LOWESS smoothing.
• More categorical encodings: Backward difference, Forward difference, Helmert, reverse Helmert, orthogonal polynomial encoding.
• Non-central chi-square, non-central F, non-central beta, and non-central t distributions.
• Anderson Darling distribution is now public.

Want to go further back? See what was new in version 6.0.