C# (CSharp) YAMP.Numerics Namespace

Nested Namespaces

YAMP.Numerics.Optimization

Classes

Name Description
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
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
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
RadixFourTransformlet
RadixSevenTransformlet
RadixThreeTransformlet
RadixTwoTransformlet
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
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.