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# Untitled

a guest Jun 19th, 2019 74 Never
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1. 2.06733483062522    -0.615946742374531
2. 2.25465524043858    -0.428626332561163
3. 1.13788162420748    -1.54539994879227
4. 1.32520203402085    -1.35807953897890
5. 0.802454971005455   -1.88082660199429
6. 0.989775380818824   -1.69350619218092
7. 0.743382354827609   -1.93989921817214
8. 0.930702764640978   -1.75257880835877
9. 0.741429028102197   -1.94185254489755
10. 0.928749437915565   -1.7545321350841
11.
12. x1 = [2.06733~ ; 2.25465~]
13.
14. % INPUT
15. % f root function (may be vector valued )
16. % df derivative of f (or function returning Jacobian matrix )
17. % x0 initial guess
18. % tol desired tolerance
19. % maxIt maximum number of iterations
20. %
21. % OUTPUT
22. % x approximate solution
23. % success true means converged according to error estimator
24. % errEst error estimate per iteration
25. % xHist ( optional ) array with intermediate solutions
26.
27. function [x, success , errEst, xHist ] = newton (f, df, x0 , tol , maxIt )
28. errEst = []; xHist = [];
29. for k = 1:1:size(f)
30.
31. iter = 0; err = inf; x = x0; success = false;
32.
33.
34. while err > 0 && iter < maxIt
35.       Fun = f{k}(x);
36.       Jac = df{k}(x);
37.       delta = -JacFun;
38.       err = norm(delta);
39.       x = x + delta;
40.       errEst = [errEst; err];
41.       xHist = [xHist; x];
42.       iter = iter + 1;
43. end
44. end
45. if err < tol
46.     success = true;
47. end
48.
49. end
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