Hi chums, I am trying to turn a matlab script into C, and have a problem with one line:Where A is a matrix that m~=n and b is a column matrix of proportions m,1.Code:X = A\b

The full script I am trying to convert (and have up until that point) is:

It is used to generate the response function, using SVD (single value decomposition), of a digital image process by finding the pixel value difference over a set number of known exposure images.Code:% % gsolve.m − Solve for imaging system response function % % Given a set of pixel values observed for several pixels in several % images with different exposure times, this function returns the % imaging system’s response function g as well as the log film irradiance % values for the observed pixels. % % Assumes: % % Zmin = 0 % Zmax = 255 % % Arguments: % % Z(i,j) is the pixel values of pixel location number i in image j % B(j) is the log delta t, or log shutter speed, for image j % l is lamdba, the constant that determines the amount of smoothness % w(z) is the weighting function value for pixel value z % % Returns: % % g(z) is the log exposure corresponding to pixel value z % lE(i) is the log film irradiance at pixel location i % function [g,lE]=gsolve(Z,B,l,w) n = 256; A = zeros(size(Z,1)*size(Z,2)+n+1,n+size(Z,1)); b = zeros(size(A,1),1); %% Include the data−fitting equations k = 1; for i=1:size(Z,1) for j=1:size(Z,2) wij = w(Z(i,j)+1); A(k,Z(i,j)+1) = wij; A(k,n+i) = −wij; b(k,1) = wij * B(i,j); k=k+1; end end %% Fix the curve by setting its middle value to 0 A(k,129) = 1; k=k+1; %% Include the smoothness equations for i=1:n−2 A(k,i)=l*w(i+1); A(k,i+1)=−2*l*w(i+1); A(k,i+2)=l*w(i+1); k=k+1; end %% Solve the system using SVD x = A\b; g = x(1:n); lE = x(n+1:size(x,1));

Matlab help defines '\' as:

Thanks in advance for any help,Code:\ Backslash or matrix left division. If A is a square matrix, A\B is roughly the same as inv(A)*B, except it is computed in a different way. If A is an n-by-n matrix and B is a column vector with n components, or a matrix with several such columns, then X = A\B is the solution to the equation AX = B computed by Gaussian elimination. A warning message is displayed if A is badly scaled or nearly singular. See the reference page for mldivide for more information. If A is an m-by-n matrix with m ~= n and B is a column vector with m components, or a matrix with several such columns, then X = A\B is the solution in the least squares sense to the under- or overdetermined system of equations AX = B. The effective rank, k, of A is determined from the QR decomposition with pivoting (see Algorithm for details). A solution X is computed that has at most k nonzero components per column. If k < n, this is usually not the same solution as pinv(A)*B, which is the least squares solution with the smallest norm

Giant.

p.s. If you can think of a better way of doing SVD with C, don't hesitate to tell me , I have run into a total brick wall here....