sirt.zip_SIRT 反演_SIRT预迭代_ride6z9_sirt算法_反演

function [s,x]=sirt(A,b,tolx,maxiter) % Syntax: x=sirt(A,b,tolx,maxiter) % % Inputs: % A = Constraint matrix.约束矩阵 % b = right hand side. % tolx = Terminate when the relative diff between % two iterations is less than tolx. % 当两个迭代之间的相对差异小于tolx时终止。 % maxiter = Stop after maxiter iterations. % 麦克斯特后停止迭代 % Outputs: % x = Solution.结果 % cccccccccccccccccccccccccccccccccccccccccccccccccccccccc % First, get the size of A. [m,n]=size(A); % % In the A1 array, we convert all nonzero entries in A to +1. A1=A>0; % % Start with the zero solution. x=zeros(n,1); % % Start the iteration count at 0. iter=0; % % Precompute N(i) and L(i) factors. N=zeros(m,1); L=zeros(m,1); for i=1:m N(i)=sum(A1(i,:)); L(i)=sum(A(i,:)); end; % % Now, the main loop. while (1==1) % % Check to make sure that we haven?t exceeded maxiter. iter=iter+1; if (iter > maxiter) disp('Max Iterations Exceeded.') return; end; % % Start the next round of updates with the current solution. newx=x; % % Now, compute the updates for all of the rays and % all cells, and put them in a vector called deltax. % deltax=zeros(n,1); for i=1:m % % Compute the approximate travel time for ray i. q=A1(i,:)*newx; % % Perform updates for those cells touched by ray i. for j=1:n if (A(i,j)>0) % % We can use either of our two approximate formulas for the update. % deltax(j)=deltax(j)+(b(i)-q)/N(i); deltax(j)=deltax(j)+b(i)/L(i)-q/N(i); end; end; end; % % Now, add the average of the updates to the old model. % newx=newx+deltax/m; % % Check for convergence % if (norm(newx-x)/(1+norm(x)) < tolx) return; end; % % No convergence, so setup for the next major iteration. % x=newx; s=post2grid(x); % end; % cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc function g=post2grid(p) g=zeros(4,4); % Post to Grid for i=1:4 for j=1:4 ndx=4*(i-1)+j; g(i,j)=p(ndx); end; end;