Extreme Optimization Numerical Libraries for .NET 7.0

.NET Core 1.1と2.1、.NET Standard 1.3と2.0をサポート
11月 30, 2018 - 12:14


NET Core and .NET Standard support

  • Support for Microsoft .NET Core 1.1 and 2.1.
  • Support for Microsoft .NET Standard 1.3 and 2.0.
  • Support for Microsoft .NET Framework 3.5, 4.0, 4.72 and later.

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


  • 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.
Extreme Optimization Numerical Libraries for .NET

Extreme Optimization Numerical Libraries for .NET

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