Software Listing: Least Squares
- Orthogonal Least Squares Algorithms for Sparse Signal Reconstruction
- License: Freeware
- Price: 0.00

OLS - Orthogonal Least Squares: Proposed by T. Blumensath, M. E. Davies StOLS - Stagewise OLS: Combining StOMP ideas with OLS ROLS - Regularized OLS: Combining ROMP ideas with OLS.
- Publisher: Angshul Majumdar
- Date: 07-05-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Discrete Least-Squares Rational Approximation
- License: Shareware

Constructs discrete least-squares rational approximations to data using the full-Newton algorithm for solving separable non-linear least-squares problems that was developed in: Carlos F. Borges, A Full-Newton Approach to Separable Nonlinear Least Squares Problems and its Application to Discrete Least Squares Rational Approximation, Electronic Transactions on Numerical Analysis, Volume 35, pp. 57-68, 2009..
- Publisher: Carlos Borges
- Date: 27-06-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Constrained Hermite Taylor Series Least Squares
- License: Shareware

Like the finite difference method, the Taylor Series Least Squares method can be used to estimate derivatives. The TLS technique can be used to estimate derivatives from scattered or unstructured data. The Hermite Taylor Series Least Squares technique augments the TLS approach with information about the derivative of the function. The Constrained Hermite Taylor Series Least Squares technique augments the HTLS technique by constraining the least squares problem to match the derivative at the point of interest. This method is fully developed in: McDonald, Robert A. and Ramos, Alejandro, 'Constrained Hermite Interpolation for Mesh-Free Derivative Estimation Near and On Boundaries', AIAA Journal, October 2011 vol.
- Publisher: Rob McDonald
- Date: 23-04-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Least Squares Fit Routine
- License: Freeware
- Price: -

Least Squares Fit Routine (LuSiFeR) is a program for least squares data analysis. Least Squares Fit Routine (LuSiFeR) is a software application that was designed for least squares data analysis. The first package is in the form of a script for MATLAB, while the second is a standalone program written in C++, with extended capabilities.Both packages perform least-squares regression analysis on a user-supplied dataset, and calculate appropriate fitted parameters (with uncertainties) and an associated X2 value.. ..
- Publisher: adampetrus.pwp.blueyonder.co.uk
- Date: 17-08-2009
- Size: 6051 KB
- Platform: WinOther
- LMFsolve.m: Levenberg-Marquardt-Fletcher algorithm for nonlinear least squares problems
- License: Freeware
- Price: 0.00

The function LMFsolve.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago. LMFsolve is its essentially shortened version implemented in MATLAB and complemented by setting iteration parameters as options. This part of the code has been strongly influenced by Duane Hanselman's function mmfsolve.m. Next to it, a finite difference approximation of Jacobian matrix is appended to it as a nested subfunction as well as a function for dispaying of intermediate results.
- Publisher: Miroslav Balda
- Date: 01-02-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Moving Least Squares
- License: Shareware

This package contains a set of tools that allows to deform in real-time points and images using the Moving Least Squares algorithms. This is a fast technique to get good image deformations without using the computational expansive techniques provided by the thin-plates splines algorithms. The algorithm was published in the paper "Image Deformation Using Moving Least Squares" by Scott Schaefer, Travis McPhail, Joe Warren.
- Publisher: Gabriele Lombardi
- Date: 03-03-2013
- Size: 1157 KB
- Platform: Matlab, Scripts
- LMFnlsq - Solution of nonlinear least squares
- License: Freeware
- Price: 0.00

The function The LMFnlsq.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago (see the Reference). This version of LMFnlsq is its complete MATLAB implementation complemented by setting parameters of iterations as options. This part of the code has been strongly influenced by Duane Hanselman's function mmfsolve.m. Calling of the function is rather simple and is one of the following: LMFnlsq % for help output x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0,'Name',Value,.
- Publisher: Miroslav Balda
- Date: 05-06-2013
- Size: 870 KB
- Platform: Matlab, Scripts
- weighted total least squares straight line fit
- License: Shareware

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that a) global convergence is assured b) a solution is found even for a vertical line. The complete uncertainty matrix (i.e. variances AND covariance of the fitting parameters) is determined. For non-vertical straight lines the usual parameters (slope/y-axis intersect.) are also given, together with their uncertainty matrix.
- Publisher: Mathias Anton
- Date: 13-02-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- weighted total least squares for mutually correlated coordinates
- License: Shareware

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that a) global convergence is assured b) a solution is found even for a vertical line. The complete uncertainty matrix (i.e. variances AND covariance of the fitting parameters) is determined. For non-vertical straight lines the usual parameters (slope/y-axis intersect.) are also given, together with their uncertainty matrix.
- Publisher: Mathias Anton
- Date: 01-01-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Total Least Squares Method
- License: Shareware

We present a Matlab toolbox which can solve basic problems related to the Total Least Squares (TLS) method in the modeling. By illustrative examples we show how to use the TLS method for solution of: - linear regression model - nonlinear regression model - fitting data in 3D space - identification of dynamical system This toolbox requires another two functions, which are already published in Matlab Central File Exchange. Those functions will be installed to computer via supporting install package 'requireFEXpackage' included in TLS package. For more details see ReadMe.txt file. Authors: Ivo Petras, Dagmar Bednarova, Tomas Skovranek, and Igor Podlubny (Technical University of Kosice, Slovakia) Detailed description of the functions, examples and demos can be found at the link: Ivo Petras and Dagmar Bednarova: Total Least Squares...
- Publisher: Ivo Petras
- Date: 09-04-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Analytical solution for Orthogonal Linear Least Squares in two dimensions
- License: Shareware

ORTHLLS2D returns the Orthogonal Linear Least Squares estimate for parameters of line a x + b y + c = 0 function f = OrthLLS2D(x, y) Inputs x and y must be real vectors of equal size. Output f is the real vector [a b c] where a, b and c are the estimated parameters of the linear equation. Since a more general function called LINORTFITN has already been submitted to File Exchange (ID number: 16800) in October 2007 by Mr. F. Carr, my file is supposed to be used as a brief introduction to the analytical problem in an extremely simple case. Orthogonal Least Squares Estimate on a plane, in the simple case of a linear equation, is in fact a problem that can be easily solved analytically with no approximation (see pdf file for detailed explanation).
- Publisher: Francesco Pozzi
- Date: 12-06-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Least squares with minimum-norm solution
- License: Shareware

This function calculates the minimum-norm solution of the least squares problem A*X = B. Where A is low-rank matrix. The function LSMIN is faster then the matlab alternative X = pinv(A)*B. Uses the LAPACK functions (S,C,D,Z)EGLSS or (S,C,D,Z)EGLSD..
- Publisher: Ivo Houtzager
- Date: 12-01-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Least Squares Data Fitting in MATLAB
- License: Shareware

Demonstration of least squares data fitting using both inverse and backslash operators. This example was developed for use in teaching modeling, simulation, and optimization in graduate engineering courses. A corresponding video is available at:.
- Publisher: James Allison
- Date: 26-06-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Least Squares Arc Calculator
- License: Shareware
- Price: 2.95

Calculate a least squares arc fitted solution to a set of points stored on your device's storage. App scans for, and displays only PNEZD (comma delimited file - Point Number, Northing, Easting, Elevation, Description) formatted text files placed on your device. Each file should contain only those points to be least squares arc fitted. After initial calculation, select point from list to change weight of any point, or choose to include / exclude any point, and recalculate. List shows perpendicular offsets to calculated arc, weighting of each point, and if the point has been included in the solution.
- Publisher: Inductive Prototype Solutions, LLC
- Date: 28-10-2014
- Size: 50 KB
- Platform: Android 3.x, Android 4.4, Android 4.x
- Linear Least Squares
- License: Freeware
- Price: 0.00

This application calculates the angular and linear coefficients of a linear regression considering the Linear Least Squares methodology..
- Publisher: Prof. Braga
- Date: 22-03-2014
- Size: 282 KB
- Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
- 3D Least squares polynomial fit in x and y
- License: Shareware

Often, measured data is comprised of N sampled values of z, evaluated at N locations (x,y). With this function, you can calculate the coefficients of the best-fit x,y polynomial using a linear least squares approximation. You can use this function if you have a set of N data triplets x,y,z, and you want to find a polynomial f(x,y) of a specific form (i.e. you know the terms you want to include (e.g. x^2, xy^3, constant, x^-3, etc.) in your fitting polynomial..
- Publisher: Thomas
- Date: 06-01-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Multiple Coherence Function
- License: Freeware
- Price: 0.00

For a system having multiple inputs x and outputs y, the partial coherence is the coherence computed between any individual input and the output when the effect of all other inputs is removed from the output by a linear least squares prediction. This coherence obeys the usual inequality, and will reveal the existence of a linear relationship between a particular residual input and the output even when the relationship is not apparent from the ordinary coherence function.
- Publisher: Mehmet Murat Altug Bicak
- Date: 25-04-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- nnls
- License: Freeware
- Price: 0.00

solves the linear least squares problem with nonnegative variables using the block principal pivoting algorithm in: Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems with nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643.
- Publisher: Uriel Roque
- Date: 19-04-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- Unimodal regression
- License: Shareware

M-files for unimodality (or monotonically) constrained least squares regression..
- Publisher: Rasmus Bro
- Date: 09-03-2013
- Size: 10 KB
- Platform: Matlab, Scripts
- dmgps
- License: Freeware
- Price: 0.00

The design matrix of a GPS network is established for any kind of least squares adjustment, namely; -free (trace minimum), -minimum constrained, -over-determined. The user can adapt easily dmgps to the adjustment problem of a GPS network whose baselines are taken as observations. Moreover, it can be used for optimization of GPS networks..
- Publisher: MATLAB 6.1 (R12.1)
- Date: 09-01-2013
- Size: 10 KB
- Platform: Matlab, Scripts









