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windows default iconNMath Publisher's description

Complete .NET mathematical library. Matrix, vector, high-performance computations.

NMath contains vector, matrix, and complex number classes, integration, sparse matrix, linear programming, least squares, polynomials, minimization, factorizations (LU, Bunch-Kaufman, and Cholesky), orthogonal decompositions (QR and SVD), advanced least squares classes (Cholesky, QR, and SVD), optimization, solver, root-finding, curve-fitting, random number generation from various probability distributions the uniform, normal, Poisson, gamma, binomial, exponential, Pareto, and log normal distributions, sparse matrix classes (general, triangular, symmetric, Hermitian, banded, tridiagonal, symmetric banded, and Hermitian banded).

Overall, NMath will provide you with comprehensive .NET mathematical components.

Full-featured vector and matrix classes for four datatypes: single- and double-precision floating point numbers, and single- and double-precision complex numbers.
Flexible indexing using slices and ranges.
Overloaded arithmetic operators with their conventional meanings for those .NET languages that support them, and equivalent named methods (Add(), Subtract(), and so on) for those that do not.
Full-featured structured sparse matrix classes, including triangular, symmetric, Hermitian, banded, tridiagonal, symmetric banded, and Hermitian banded.
Functions for converting between general matrices and structured sparse matrix types.
Functions for transposing structured sparse matrices, computing inner products, and calculating matrix norms.
Classes for factoring structured sparse matrices, including LU factorization for banded and tridiagonal matrices, Bunch-Kaufman factorization for symmetric and Hermitian matrices, and Cholesky decomposition for symmetric and Hermitian positive definite matrices. Once constructed, matrix factorizations can be used to solve linear systems and compute determinants, inverses, and condition numbers.
General sparse vector and matrix classes, and matrix factorizations.
Orthogonal decomposition classes for general matrices, including QR decomposition and singular value decomposition (SVD).
Advanced least squares factorization classes for general matrices, including Cholesky, QR, and SVD.
LU factorization for general matrices, as well as functions for solving linear systems, computing determinants, inverses, and condition numbers.
Classes for solving symmetric, Hermitian, and nonsymmetric eigenvalue problems.
Extension of standard mathematical functions, such as Cos(), Sqrt(), and Exp(), to work with vectors, matrices, and complex number classes.


Classes for encapsulating functions of one variable, with support for numerical integration (Romberg and Gauss-Kronrod methods), differentiation (Ridders' method), and algebraic manipulation of functions.
Polynomial encapsulation, interpolation, and exact differentiation and integration.
Classes for minimizing univariate functions using golden section search and Brent's method.
Classes for minimizing multivariate functions using the downhill simplex method, Powell's direction set method, the conjugate gradient method, and the variable metric (or quasi-Newton) method.
Simulated annealing.
Linear Programming (LP), Non-Linear Programming (NLP), and Quadratic Programming (QP).
Least squares polynomial fitting.
Nonlinear least squares minimization, curve fitting, and surface fitting.
Classes for finding roots of univariate functions using the secant method, Ridders' method, and the Newton-Raphson method.
Numerical methods for double integration of functions of two variables.
Nonlinear least squares minimization using the Trust-Region method, a variant of the Levenberg-Marquardt method.
Curve and surface fitting by nonlinear least squares.
Classes for solving first order initial value differential equations by the Runge-Kutta method.

What's New in This Release:

· Upgraded to Intel MKL 10.3 Update 11 with resulting performance increases.
· Added class NMathConfiguration for controlling the loading of the NMath license key, kernel assembly, and native library. License files are no longer used. Logging can be enabled for debugging configuration issues.
· Replaced all custom NMath delegate types in the API with Func/Action equivalents, and deprecated the older signatures.
· Added support for postive and negative strided signals in all FFT classes.
· Fixed bug in DoubleSymmetricSignalReader.UnpackSymmetricHalfToVector(), in which the last element was not read.
· Fixed bug in ResizeAndClear() of vector classes.
· Removed previously deprecated OneVariableFunctionFitter and MultiVariableFunctionFitter classes. Use templatized OneVariableFunctionFitter and MultiVariableFunctionFitter instead.
· Made Slice.All and Range.All singletons for greater efficiency.
· Exposed OptimalX property of quadratic programmming classes.
· Added Set() method to all matrix and vector classes for setting all elements to a given value.

System Requirements:

· Microsoft .NET Framework
Program Release Status: New Release
Program Install Support: Install and Uninstall

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