# CS代写 function [Amod,bmod]=gauss_el(A,b,display,pivot) – cscodehelp代写

function [Amod,bmod]=gauss_el(A,b,display,pivot)
% On entry: A is a square matrix
% b is a column vector of the same dimension
% display is 1 if step-by-step display desired, 0 otherwise

% On exit: Amod and bmod are the result of applying Gauss elimination to
% the system Ax=b. If pivot == 0, no row or column pivoting is done, so the
% process is unstable and may break down even if A is nonsingular.
% If pivot = 1, do partial pivoting (row interchanges)
n = length(b);
if size(A) ~= [n,n] | size(b) ~=[n,1],
error(‘mismatched dimension’)
for k = 1:n-1, % the kth step eliminates the kth column below the diagonal
if pivot % partial pivoting (row interchanges)
% find index q with largest entry in absolute value on or below the
% diagonal in column k
[maxval,q] = max(abs(A(k:n,k)));
q = q + k-1; % want index between k and n
A([k q],:) = A([q k],:); % swap rows k and q (ok if q == k)
b([k q]) = b([q k]);
for i = k+1:n, % We need to work on rows k+1 to n of the kth column
mult = A(i,k)/A(k,k); % multiplier which will give desired zero
% A(i,:) = A(i,:) – mult*A(k,:); % subtract mult*(kth row) to ith row
A(i,k+1:n) = A(i,k+1:n) – mult*A(k,k+1:n); % don’t need to compute the zeros
A(i,k) = 0; % so looks nice – or, could store mult here
b(i) = b(i) – mult*b(k); % apply same row operation to b
if display

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