Task 2 more functions

ENUME/projectA/jacobiMethod.asv

@@ -1,43 +1,69 @@
 function x = jacobiMethod(Matrix, Vector)
-    [Rows,~] = size(Matrix);
+    [L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance] = initializeValues(Matrix);
+    [x, whichIterationAreWeOn, demandedTolerance] = jacobiLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance, Vector);
+    dispFinalResults(demandedTolerance, whichIterationAreWeOn, Matrix, Vector);
+end
+
+function [L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance] = initializeValues(Matrix)
+    [Rows, ~] = size(Matrix);
+    [L, D, U] = decomposeMatrix(Matrix);
+    initial_x = ones(Rows, 1);
+    whichIterationAreWeOn = 0;
+    currentError = inf; % We set it to inf so that the algorithm will always start
+    % (See condition below)
+    demandedTolerance = 1e-10;
+end
+
+function [L, D, U] = decomposeMatrix(Matrix)
     D = diag(diag(Matrix));
-    inverseD = inv(D);
     U = triu(Matrix, 1); % Generates upper triangular part of matrix
     % where the second variable denotes on which diagonal of matrix should we
     % start
     L = tril(Matrix, -1); % Generates lower triangular part of matrix
     % where the second variable denotes on which diagonal of matrix should we
     % start
-    initial_x = ones(Rows, 1);
+end
-    initial_x = - inverseD * ( ( L + U ) * initial_x) + inverseD * initial_x; % As per formula
+
-    % We will be using D \ initial_x and D \ () since it is faster and more
+function [x, whichIterationAreWeOn, demandedTolerance]  = jacobiLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance, Vector)
-    % accurate according to matlab
-    whichIterationAreWeOn = 0;
-    currentError = inf; % We set it to inf so that it the algorithm will always start
-    % (See condition below)
-    demandedTolerance = 1e-10;
     while currentError >= demandedTolerance
-      
+        x = jacobiEquation(D, L, U, initial_x, Vector);
-        x = - inverseD * ( ( L + U ) * initial_x) + inverseD * initial_x; % As per formula
+        [flag, demandedTolerance] = checkError(x, initial_x, demandedTolerance, Matrix, Vector);
-        
+        if flag == 1
-        currentError = norm(x - initial_x);
+            break
-        disp(currentError);
-        if currentError <= demandedTolerance
-            currentError = norm(Matrix*x-Vector);
-            disp(currentError);
-            if currentError <= demandedTolerance
-                break;
-            else 
-                demandedTolerance = demandedTolerance * 2;
-            end
         end
         initial_x = x;
         whichIterationAreWeOn = whichIterationAreWeOn + 1;
     end
+end
+
+function x = jacobiEquation(D, L, U, initial_x, Vector)
+    x = - D \ ( L + U ) * initial_x + D \ Vector; % As per formula
+    % We will be using D \ Vector and D \ ( ) instead of inverseD since
+    % this is faster according to matlab
+end
+
+function [flag, demandedTolerance] = checkError(x, initial_x, demandedTolerance, Matrix, Vector)
+        flag = 0;
+        currentError = norm(x - initial_x);
+        if currentError <= demandedTolerance
+            currentError = norm(Matrix*x-Vector);
+            if currentError <= demandedTolerance
+                flag = 1;
+            else 
+                demandedTolerance = demandedTolerance * 2;
+            end
+        end
+end
+
+function [initial_x, ]
+
+
+
+function dispFinalResults(demandedTolerance, whichIterationAreWeOn, Matrix, Vector)
     disp("Final demandedTolerance");
     disp(demandedTolerance);
     disp("Final Iteration: ");
     disp(whichIterationAreWeOn);
     disp("A\b matlab:");
-    disp(Matrix / Vector);
+    disp(Matrix \ Vector);
 end

ENUME/projectA/jacobiMethod.m

@@ -1,22 +1,16 @@
 function x = jacobiMethod(Matrix, Vector)
-    [L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance] = initializeValues(Matrix)
+    [L, D, U, initial_x, whichIterationAreWeOn, demandedTolerance, flag] = initializeValues(Matrix);
-    x = jacobiLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance, Vector);
+    [x, whichIterationAreWeOn, demandedTolerance] = jacobiLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, demandedTolerance, Vector, flag);
-    disp("Final demandedTolerance");
+    dispFinalResults(demandedTolerance, whichIterationAreWeOn, Matrix, Vector);
-    disp(demandedTolerance);
-    disp("Final Iteration: ");
-    disp(whichIterationAreWeOn);
-    disp("A\b matlab:");
-    disp(Matrix \ Vector);
 end
 
-function [L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance] = initializeValues(Matrix)
+function [L, D, U, initial_x, whichIterationAreWeOn, demandedTolerance, flag] = initializeValues(Matrix)
     [Rows, ~] = size(Matrix);
     [L, D, U] = decomposeMatrix(Matrix);
     initial_x = ones(Rows, 1);
     whichIterationAreWeOn = 0;
-    currentError = inf; % We set it to inf so that the algorithm will always start
-    % (See condition below)
     demandedTolerance = 1e-10;
+    flag = 0;
 end
 
 function [L, D, U] = decomposeMatrix(Matrix)
@@ -29,28 +23,50 @@ function [L, D, U] = decomposeMatrix(Matrix)
     % start
 end
 
-function x = jacobiLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, currentError, demandedTolerance, Vector)
+function [x, whichIterationAreWeOn, demandedTolerance]  = jacobiLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, demandedTolerance, Vector, flag)
-    while currentError >= demandedTolerance
+    while flag ~= 1
-        x = jacobiEquation(D, L, U, initial_x, Vector);
+        [x, whichIterationAreWeOn, demandedTolerance, flag, initial_x] = jacobiInsideLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, demandedTolerance, Vector);
-        currentError = norm(x - initial_x);
-        %disp(currentError);
-        if currentError <= demandedTolerance
-            currentError = norm(Matrix*x-Vector);
-            %disp(currentError);
-            if currentError <= demandedTolerance
-                break;
-            else 
-                demandedTolerance = demandedTolerance * 2;
-            end
-        end
-        initial_x = x;
-        whichIterationAreWeOn = whichIterationAreWeOn + 1;
     end
 end
 
+function [x, whichIterationAreWeOn, demandedTolerance, flag, initial_x] = jacobiInsideLoop(Matrix, L, D, U, initial_x, whichIterationAreWeOn, demandedTolerance, Vector)
+    x = jacobiEquation(D, L, U, initial_x, Vector);
+    [flag, demandedTolerance] = checkError(x, initial_x, demandedTolerance, Matrix, Vector);
+    [initial_x, whichIterationAreWeOn] = endOfLoop(x, whichIterationAreWeOn);
+end
 
 function x = jacobiEquation(D, L, U, initial_x, Vector)
     x = - D \ ( L + U ) * initial_x + D \ Vector; % As per formula
     % We will be using D \ Vector and D \ ( ) instead of inverseD since
     % this is faster according to matlab
+end
+
+function [flag, demandedTolerance] = checkError(x, initial_x, demandedTolerance, Matrix, Vector)
+        flag = 0;
+        currentError = norm(x - initial_x);
+        if currentError <= demandedTolerance
+            currentError = norm(Matrix*x-Vector);
+            if currentError <= demandedTolerance
+                flag = 1;
+            else 
+                demandedTolerance = demandedTolerance * 2;
+            end
+        end
+end
+
+function [initial_x, whichIterationAreWeOn, flag] = endOfLoop(x, whichIterationAreWeOn)
+    initial_x = x;
+    whichIterationAreWeOn = whichIterationAreWeOn + 1;
+    flag = 0;
+end
+ 
+
+
+function dispFinalResults(demandedTolerance, whichIterationAreWeOn, Matrix, Vector)
+    disp("Final demandedTolerance");
+    disp(demandedTolerance);
+    disp("Final Iteration: ");
+    disp(whichIterationAreWeOn);
+    disp("A\b matlab:");
+    disp(Matrix \ Vector);
 end