| 이름 |
설명 |
| Bessel |
This class contains bessel functions. |
| BinomialDistribution |
Provides generation of binomial distributed random numbers. |
| BlasL1 |
Provides BLAS Level 1 Access, this level contains vector operations of the form y = a * x + y. |
| BlasL2 |
Provides BLAS Level 2 Access, this level contains matrix-vector operations of the form y = A * x + y. |
| BlasL3 |
Blas Level 3 Matrix Matrix multiplication. |
| BluesteinTransformlet |
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| CGSolver |
Basic class for a Conjugant Gradient solver. |
| CholeskyDecomposition |
Cholesky Decomposition. For a symmetric, positive definite matrix A, the Cholesky decomposition is an lower triangular matrix L so that A = L*L'. If the matrix is not symmetric or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the isSPD() method. |
| ContinuousUniformDistribution |
Provides generation of continuous uniformly distributed random numbers. |
| Dawson |
This class contains the dawson integral. |
| DirectSolver |
Abstract base class for any (direct) solver. |
| DiscreteUniformDistribution |
Provides generation of discrete uniformly distributed random numbers. |
| Distribution |
Declares common functionality for all random number distributions. |
| Eigenvalues |
Eigenvalues and eigenvectors of a real matrix. If A is symmetric, then A = V * D * V' where the eigenvalue matrix D is diagonal and the eigenvector matrix V is orthogonal. I.e. A = V.Multiply(D.Multiply(V.Transpose())) and V.Multiply(V.Transpose()) equals the identity matrix. If A is not symmetric, then the eigenvalue matrix D is block diagonal with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda]. The columns of V represent the eigenvectors in the sense that A * V = V * D, i.e. A.Multiply(V) equals V.Multiply(D). The matrix V may be badly conditioned, or even singular, so the validity of the equation A = V * D * Inverse(V) depends upon V.cond(). |
| ErrorFunction |
This class contains everything about the error function. |
| Euler |
Represents the Euler algorithm for computing ODEs. |
| ExponentialDistribution |
Provides generation of exponential distributed random numbers. |
| FFT |
A simple FFT implemention that uses Cooley-Tukey FFT (i.e. 2^n elements required). |
| Fourier |
A more advanced FFT that is a lot more general. |
| Fourier.Factor |
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| Fractal |
Represents the abstract base class for Fractals. |
| GMRESkSolver |
Basic class for a GMRES(k) (with restarts) solver. |
| Gamma |
This class contains the linear gamma function as well as complex ones and logarithmic ones. |
| GammaDistribution |
Provides generation of gamma distributed random numbers. |
| Generator |
Declares common functionality for all random number generators. |
| GivensDecomposition |
The Givens rotation is an implementation of a QR decomposition. This decomposition also works for complex numbers. |
| GoldenSection |
The golden section search is a technique for finding the extremum (minimum or maximum) of a strictly unimodal function by successively narrowing the range of values inside which the extremum is known to exist. |
| HalfDivisionMethod |
Access to the half division method for getting the closest root. |
| Helpers |
Provides some commonly used methods for numeric algorithms. |
| Helpers.ChebSeries |
The coefficients with order, and more information. |
| HouseholderDecomposition |
The Householder reflection is an implementation of a QR decomposition. This decomposition does not work for complex numbers. |
| Integrator |
The abstract base class for every integrator algorithm. |
| Interpolation |
Abstract base class for various interpolation algorithms. |
| IterativeSolver |
The abstract base class for any iterative solver. |
| Julia |
This is the (more general) Julia fractal (superset of the Mandelbrot set). |
| LUDecomposition |
LU Decomposition. For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U. If m is smaller than n, then L is m-by-m and U is m-by-n. The LU decompostion with pivoting always exists, even if the matrix is singular, so the constructor will never fail. The primary use of the LU decomposition is in the solution of square systems of simultaneous linear equations. This will fail if IsNonSingular() returns false. |
| LaplaceDistribution |
Provides generation of laplace distributed random numbers. |
| MT19937Generator |
Represents a Mersenne Twister pseudo-random number generator with period 2^19937-1. |
| Mandelbrot |
Creates the class for evaluating a mandelbrot function. |
| NevilleInterpolator |
The Neville polynom interpolation algorithm. |
| Newton |
This is the so called Newton fractal. |
| NewtonInterpolation |
The Newton polynomial interpolation method. |
| NonLinearBase |
Abstract base class for all non-linear algorithms to determine the closest root. |
| NormalDistribution |
Provides generation of normal distributed random numbers. |
| ODEBase |
Abstract base class for all ODE algorithms. |
| OptimizationBase |
The abstract base class for all optimization algorithms, i.e. the ones to find an extremum. |
| Pijavsky |
Represents the Pijavsky algorithm for optimizing. |
| PoissonDistribution |
Provides generation of poisson distributed random numbers. |
| QRDecomposition |
QR Decomposition. For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R so that A = Q * R. The QR decompostion always exists, even if the matrix does not have full rank, so the constructor will never fail. The primary use of the QR decomposition is in the least squares solution of nonsquare systems of simultaneous linear equations. This will fail if IsFullRank() returns false. |
| RadixFiveTransformlet |
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| RadixFourTransformlet |
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| RadixSevenTransformlet |
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| RadixThreeTransformlet |
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| RadixTwoTransformlet |
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| RayleighDistribution |
Provides generation of rayleigh distributed random numbers. |
| RungeKutta |
This is the Runge-Kutta Algorithm for solving ODEs. |
| SecantMethod |
Represents the Secant method for determining the closest root. |
| SimpsonIntegrator |
Represents a specific algorithm for integration - Simpson's rule. |
| SingularValueDecomposition |
Singular Value Decomposition. For an m-by-n matrix A with m >= n, the singular value decomposition is an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and an n-by-n orthogonal matrix V so that A = U*S*V'. The singular values, sigma[k] = S[k][k], are ordered so that sigma[0] >= sigma[1] >= ... >= sigma[n-1]. The singular value decompostion always exists, so the constructor will never fail. The matrix condition number and the effective numerical rank can be computed from this decomposition. |
| SplineInterpolation |
Interpolation with the spline method. |
| Transformlet |
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| TrapezIntegrator |
Represents the Trapez integration algorithm - a very simple rule for numerical integration. |
| WeibullDistribution |
Provides generation of weibull distributed random numbers. |
| Zeta |
Provides access to the useful Riemann-Zeta function. |