• 介绍
• 价格
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以下是 "NMath .NET"，如果您需要了解更多信息，您可以联系我们。

基本数学

• 单双精度复杂数字类；
• 随机生成各种概率分布的数字，用跳读和跨越式方法的独立随机数字流，以及使用Niederreiter 和 Sobal方法的拟随机序列；
• 快速傅立叶(FFTs)，线性线性卷积和相关；

线性代数

• 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.

功能

• 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.

与.NET标准库的集成

• 使用标准的.NET机制创建完全可持久数据类；
• 与ADO.NET集成；
• 用Microsoft Chart Controls for .NET绘制并显示数据