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Text File | 1994-06-05 | 3.5 KB | 161 lines | [MATS/MATL] |
- echo off;
- % NUMERICAL METHODS: MATLAB Programs, (c) John H. Mathews 1994
- % To accompany the text:
- % NUMERICAL METHODS for Mathematics, Science and Engineering, 2nd Ed, 1992
- % Prentice Hall, Englewood Cliffs, New Jersey, 07632, U.S.A.
- % This free software is complements of the author.
-
- % Algorithm 2.8 (Muller's Method).
- % Section 2.5, Aitken's Process & Steffensen's & Muller's Methods, Page 97
- echo on; clc; format long; hold off; clear
- % This program implements Muller's method.
-
- % Define and store the function f(x) in the M-file f.m
- % function y = f(x)
- % y = x.^3 - 3.*x + 2;
-
- delete f.m
- diary f.m; disp('function y = f(x)');...
- disp('y = x.^3 - 3.*x + 2;');...
- diary off;
-
- % Remark. f.m and muller.m are used for Algorithm 2.8
- f(0); % Test for file f.m
- pause % Press any key to see the graph y = f(x).
-
- clc;
- % Plot f(x) over the interval [a,b].
-
- a = -2.5;
- b = 2.5;
- c = -5;
- d = 5;
- h = (b-a)/100;
- X = a:h:b;
- Y = f(X);
- axis([a b c d]);...
- plot(X,Y,'-g');...
- hold on;...
- plot([a b],[0 0],'b',[0 0],[c d],'b');...
- xlabel('x');...
- ylabel('y');...
- title('Graph of y = f(x).');...
- grid;...
- axis;...
- hold off;...
- shg; pause % Press any key to perform Muller's iteration.
-
- clc;
- % Place the starting values in p0 p1 and p2
-
- % Place the abscissa tolerance in delta
-
- % Place the ordinate tolerance in epsilon
-
- % Place the number of iterations in max1
-
- p0 = -2.6;
- p1 = -2.5;
- p2 = -2.4;
- delta = 1e-12;
- epsilon = 1e-12;
- max1 = 10;
-
- [p2,y2,err,P] = muller('f',p0,p1,p2,delta,epsilon,max1);
-
- pause % Press any key for the list of iterations.
-
- clc; clg;
- a = -2.65;
- b = -1.95;
- c = -9;
- d = 1;
- h = (b-a)/100;
- X = a:h:b;
- Y = f(X);
- max1 = length(P);
- n0 = min(7,max1);
- X0 = P(1:n0);
- Z0 = zeros(1,n0);
- axis([a b c d]);...
- plot(X,Y,'-g',X0,Z0,'or');...
- hold on;...
- plot([a b],[0 0],'b',[0 0],[c d],'b');...
- xlabel('x');...
- ylabel('y');...
- title('Graphical analysis for Muller`s method.');...
- grid;...
- axis;...
- hold off;...
- shg; pause % Press any key to continue.
-
- J = 1:max1;
- Yp = f(P);
- points = [J;P;Yp];
- Mx1 = 'Iterations for Muller`s method.';
- Mx2 = [' k p(k) f(p(k))'];
- Mx3 = 'The solution is:';
- Mx4 = 'The error estimate for p is ± ';
- clc,echo off, diary output,...
- disp(''), disp(Mx1),disp(''), disp(Mx2), disp(points'),...
- disp('Iteration converged quadratically to the root.'),...
- disp(''),disp(Mx3),disp(''),disp('p = '),...
- disp(p2),disp('f(p) = '),disp(y2),...
- disp([Mx4,num2str(err)]),diary off,echo on
-
- pause % Press any key to perform Muller's iteration.
-
- clc;
- % Place the starting values in p0 p1 and p2
-
- % Place the abscissa tolerance in delta
-
- % Place the ordinate tolerance in epsilon
-
- % Place the number of iterations in max1
-
- p0 = 1.4;
- p1 = 1.3;
- p2 = 1.2;
- delta = 1e-12;
- epsilon = 1e-12;
- max1 = 10;
-
- [p2,y2,err,P] = muller('f',p0,p1,p2,delta,epsilon,max1);
-
- pause % Press any key for the list of iterations.
-
- clc; clg;
- a = 0.975;
- b = 1.425;
- c = -0.05;
- d = 0.55;
- h = (b-a)/100;
- X = a:h:b;
- Y = f(X);
- max1 = length(P);
- n0 = min(7,max1);
- X0 = P(1:n0);
- Z0 = zeros(1,n0);
- axis([a b c d]);...
- plot(X,Y,'-g',X0,Z0,'or');...
- hold on;...
- plot([a b],[0 0],'b',[0 0],[c d],'b');...
- xlabel('x');...
- ylabel('y');...
- title('Graphical analysis for Muller`s method.');...
- grid;...
- axis;...
- hold off;...
- shg; pause % Press any key to continue.
-
- J = 1:max1;
- Yp = f(P);
- points = [J;P;Yp];
- clc,echo off, diary on,...
- disp(''), disp(Mx1),disp(''), disp(Mx2), disp(points'),...
- disp('Iteration converged quadratically to the root.'),...
- disp(''),disp(Mx3),disp(''),disp('p = '),...
- disp(p2),disp('f(p) = '),disp(y2),...
- disp([Mx4,num2str(err)]),diary off,echo on
-