Extreme Optimization Numerical Libraries for .NET 7.0

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.

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.