This code can be used to solve a set of linear equations using Gaussian elimination with partial pivoting. Note that the Augmented matrix rows are not directly switches. Instead a buffer vector is keeping track of the switches made. The final solution is determined using backward substitution.
The "GEE! It's Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U where P is a permutation matrix, and L and U are lower and upper triangular,...
rrlu computes a rank revealing LU factorization of a general m-by-n real full matrix A using partial pivoting with row and column interchanges.
The factorization has the form A(P,Q) = L * U where P and Q are permutation...
The m-file finds the elimination matrices (and scaling matrices) to reduce any A matrix to the identity matrix using the Gauss-Jordan elimination method without pivoting. Using the matrices gotten it computes the inverse of the A matrix.
Genereates random number from the closed-skew Gaussian distribution with two different methods:
CSN_mcmc.m Does mcmc, so each realisation are not completely independent % Usage: [res, t_wi, t_inv]...
Suppose that you have a signal Y (Y can be a time series, a parametric surface or a volumetric data series) corrupted by a Gaussian noise with unknown variance. It is often of interest to know more about this variance. EVAR(Y) thus returns an...
Simulink model for 1phase fully controlled scr harmonic elimination with free wheeling is modelled. its easy to understand and efficient and u can learn the basics of rectification. It is intended to students who would like to simulate rectifiers....
Source code includes portable (Windows, Linux, and MacX) C++ libraries: Thread pool Asynchronous sockets management Asynchronous files management Completion Port implementation for Linux Database access (Oracle, MySQL,...
CSPHANTOM is a test phantom tailored to compressed sensing MRI algorithm development. It is designed to be non-sparse under a gradient transform and to contain features difficult to reproduce with partial Fourier sampling. We hope that this...
DIRECT METHODS FOR SOLUTION OF LINEAR SYSTEMS Gaussian Elimination Algorithm Gauss-Jordan Algorithm Factoring algorithm
ITERATIVE METHODS FOR SOLVING SYSTEMS OF EQUATIONS Jacobi algorithm Gauss-Seidel algorithm
This function infers the unobserved regimes and provides estimates for the parameters of a Gaussian mixture with two states using the EM algorithm.
Exact references to the relevant equations from Hamilton (1994), Chapter 22 can be found...
We focus on the lectures 20, 21, and 22 of the book "Numerical Linear Algebra" by Trefethen and Baum.
Working with Windows API which usually takes like a zillion for each function can be a little bit frustrating and if I want to only change two in the middle for each call I had to wrap everything into lambda functions which change arguments to the...
Generates complex generalized gaussian random variables with augmented covariance matrix Ta = [2*s 0; 0 2*s]; and shape parameter c, where c = 1 corresponds to the Gaussian case. x = cggd_rand(c,s,N) generates a vector 1xN of...
The Gaussian quadrature is among the most accurate integration scheme for smooth integrands. It replaces a integral by a sum of sampled values of the integrand function times some weight factors.
This is strictly a minor rewrite of...
This function returns coefficients of Gaussian lowpass filter. Advantages of Gaussian filter: no ringing or overshoot in time domain. Diasadvantage: slow rolloff in frequency domain. Pass SR=sampling rate, fco=cutoff freq, both in...
The Expectation-Maximization algorithm (EM) is widely used to find the parameters of a mixture of Gaussian probability density functions (pdfs) or briefly Gaussian components that fits the sample measurement vectors in maximum likelihood sense...
NDGAUSS: create ND gaussian kernel in any derivative order.
g = ndgauss(hsize,sigma); [g,xi,yi,..] = ndgauss(hsize,sigma);
Inputs: - hsize is N-length of kernel size. - sigma is N-length of standard...
The conjugate gradient method aims to solve a system of linear equations, Ax=b, where A is symmetric, without calculation of the inverse of A. It only requires a very small amount of membory, hence is particularly suitable for large scale...
% the water-filling process % x: a vector with each component representing noise power % P: total power % % The returned vector p maximizes the total sum rate given by % sum(log(1 + p./x)), subject to the power constraint... |