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Topic: using matlab least squares functions
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Posts: 90
Registered: 8/28/10
using matlab least squares functions
Posted: Sep 27, 2013 12:24 AM
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I have my matlab code which solves a least squares problem and gives me the right answer. My code is below. I explicitly use my own analytically-derived Jacobian and so on. I just purchased the Optimization toolbox. Can anyone perhaps show me how my code can be used via the functions provided by the Optimization toolbox such as lsqnonlin and so on.

thank you.

%=========== MY least squares ==============%
clc;clear all;close all
beep off
X = [-0.734163292085050,-0.650030660496880;-0.734202821328435,-0.650069503240265;-0.738931528235336,-0.660060466119060;-0.737943703068185,-0.670101503002962;-0.736799998431314,-0.680143905314235;]
Y = [-0.736371316036657,-0.661615260180661;-0.736372829883012,-0.661616774027016;-0.736552116163647,-0.662004318693837;-0.736510559472223,-0.662391863360658;-0.736462980793180,-0.662779408027478;]
Z = X;
w2 =10
w1 = 1;
Zo = Z;
% Create kd-tree
kd = KDTreeSearcher(Y);
% Initialize linear system ||D^0.5(Av - b)||_2^2
% A is the Jacobian
% D is a weight matrix
dim = size(Z,1)*size(Z,2);
A = sparse(2*dim, dim+3);
A(1:dim,1:dim) = speye(dim,dim);
A((1+dim):end,1:dim) = speye(dim,dim);
A((1+dim):(dim+dim/2), end-1) = -ones(dim/2,1);
A((1+dim+dim/2):end, end) = -ones(dim/2,1);
b = zeros(2*dim,1)
D = sparse(2*dim, 2*dim);
D(1:dim,1:dim) = w1*speye(dim,dim);
for it=1:nit
% kd-tree look-up
idz = knnsearch(kd,Z);
P = Y(idz,:);
% Build linear system
b(1:dim) = reshape(P,dim,1);
b((1+dim):end) = reshape(X,dim,1);
Xr = X;
Xr(:,1) = -Xr(:,1);
Xr = fliplr(Xr);
A((1+dim):end,end-2) = reshape(Xr,dim,1);
D((dim+1):end,(dim+1):end) = w2*speye(dim,dim);
% Solve Least Squares
v = (A'*D*A)\(A'*D*b);
% Extract solution
Z = reshape(v(1:dim), size(X,1), size(X,2));
theta = v(end-2);
R = [cos(theta), -sin(theta); sin(theta) cos(theta)];
X = X*R' + repmat(v((end-1):end)', [size(X,1),1]);
% Stopping Criteria
if(norm(Z-Zo)/size(Z,1) < 1e-6)
Zo = Z;

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