Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course
in numerical methods, Matlab, and technical computing. The emphasis is on in-
formed use of mathematical software. We want you learn enough about the mathe-
matical functions in Matlab that you will be able to use them correctly, appreciate
their limitations, and modify them when necessary to suit your own needs. The
topics include
* introduction to Matlab,
* linear equations,
* interpolation,
* zero and roots,
* least squares,
* quadrature,
* ordinary di?erential equations,
* random numbers,
* Fourier analysis,
* eigenvalues and singular values,
* partial di?erential equations.
Toolbox for Numerical Computing with MATLAB （by Cleve Moler).
Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course
in numerical methods, Matlab, and technical computing. The emphasis is on in-
formed use of mathematical software. We want you learn enough about the mathe-
matical functions in Matlab that you will be able to use them correctly, appreciate
their limitations, and modify them when necessary to suit your own needs. The
topics include
* introduction to Matlab,
* linear equations,
* interpolation,
* zero and roots,
* least squares,
* quadrature,
* ordinary di?erential equations,
* random numbers,
* Fourier analysis,
* eigenvalues and singular values,
* partial differential equations.
this directory
contains the following:
* The acdc algorithm for finding the
approximate general (non-orthogonal)
joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices.
[acdc.m]
* The acdc algorithm for finding the
same for a set of Symmetric matrices.
[acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version
[acdc.m], rather than the Symmetric version[acdc_sym] is preferable.
* A function that finds an initial guess
for acdc by applying hard-whitening
followed by Cardoso s orthogonal joint
diagonalizer. Note that acdc may also
be called without an initial guess,
in which case the initial guess is set by default to the identity matrix.
The m-file includes the joint_diag
function (by Cardoso) for performing
the orthogonal part.
[init4acdc.m]
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another.
KEYWORDS: Total least squares, generalized total least squares, software implementation.
The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods.
In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated
Mapack可用來做矩陣運算
Mapack is a .NET class library for basic linear algebra computations. It supports the following matrix operations and properties: Multiplication, Addition, Subtraction, Determinant, Norm1, Norm2, Frobenius Norm, Infinity Norm, Rank, Condition, Trace, Cholesky, LU, QR, Single Value decomposition, Least Squares solver, Eigenproblem solver, Equation System solver. The algorithms were adapted from Mapack for COM, Lapack and the Java Matrix Package.
observable distribution grid are investigated. A distribution
grid is observable if the state of the grid can be fully determined.
For the simulations, the modified 34-bus IEEE test feeder is used.
The measurements needed for the state estimation are generated
by the ladder iterative technique. Two methods for the state
estimation are analyzed: Weighted Least Squares and Extended
Kalman Filter. Both estimators try to find the most probable
state based on the available measurements. The result is that
the Kalman filter mostly needs less iterations and calculation
time. The disadvantage of the Kalman filter is that it needs some
foreknowlegde about the state.
This is GPS in matlab calculatePseudoranges finds relative pseudoranges for all satellites
listed in CHANNELLIST at the specified millisecond of the processed
signal. The pseudoranges contain unknown receiver clock offset. It can be
found by the least squares position search procedure.
Basic function to locate and measure the positive peaks in a noisy
data sets. Detects peaks by looking for downward zero-crossings
in the smoothed third derivative that exceed SlopeThreshold
and peak amplitudes that exceed AmpThreshold. Determines,
position, height, and approximate width of each peak by least-squares
curve-fitting the log of top part of the peak with a parabola.
最小二乘法曲面擬合,包括C程序及說明文件。對于搞三維重建的有一定幫助-Least squares surface fitting, including the C procedures and documentation. For engaging in three-dimensional reconstruction to some extent help the
