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https://github.com/kuhyx/WUT_Computer_Science.git
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43 lines
1.6 KiB
Plaintext
43 lines
1.6 KiB
Plaintext
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function x = jacobiMethod(Matrix, Vector)
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[Rows,~] = size(Matrix);
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D = diag(diag(Matrix));
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inverseD = inv(D);
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U = triu(Matrix, 1); % Generates upper triangular part of matrix
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% where the second variable denotes on which diagonal of matrix should we
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% start
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L = tril(Matrix, -1); % Generates lower triangular part of matrix
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% where the second variable denotes on which diagonal of matrix should we
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% start
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initial_x = ones(Rows, 1);
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initial_x = - inverseD * ( ( L + U ) * initial_x) + inverseD * initial_x; % As per formula
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% We will be using D \ initial_x and D \ () since it is faster and more
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% accurate according to matlab
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whichIterationAreWeOn = 0;
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currentError = inf; % We set it to inf so that it the algorithm will always start
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% (See condition below)
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demandedTolerance = 1e-10;
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while currentError >= demandedTolerance
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x = - inverseD * ( ( L + U ) * initial_x) + inverseD * initial_x; % As per formula
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currentError = norm(x - initial_x);
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disp(currentError);
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if currentError <= demandedTolerance
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currentError = norm(Matrix*x-Vector);
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disp(currentError);
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if currentError <= demandedTolerance
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break;
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else
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demandedTolerance = demandedTolerance * 2;
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end
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end
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initial_x = x;
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whichIterationAreWeOn = whichIterationAreWeOn + 1;
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end
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disp("Final demandedTolerance");
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disp(demandedTolerance);
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disp("Final Iteration: ");
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disp(whichIterationAreWeOn);
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disp("A\b matlab:");
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disp(Matrix / Vector);
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end
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