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Newton-Kantorovich Method and Its Global Convergence

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Abstract

In 1948, L. V. Kantorovich extended the Newton method for solving nonlinear equations to functional spaces. This event cannot be overestimated: the Newton-Kantorovich method became a powerful tool in numerical analysis as well as in pure mathematics. We address basic ideas of the method in historical perspective and focus on some recent applications and extensions of the method and some approaches to overcoming its local ture. Bibliography: 56 titles.

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REFERENCES

  1. L. V. Kantorovich, “On Newton's method for functional equations,” Dokl. Akad. Nauk SSSR, 59, 1237–1240 (1948).

    Google Scholar 

  2. L. V. Kantorovich, “Functional analysis and applied mathematics,” Uspekhi Mat. Nauk, 3, 89–185 (1948).

    Google Scholar 

  3. L. V. Kantorovich, “On Newton method,” Trudy Steklov Math. Inst., 28, 104–144 (1949).

    Google Scholar 

  4. L. V. Kantorovich, “Principle of majorants and Newton's method,” Dokl. Akad. Nauk SSSR, 76, 17–20 (1951).

    Google Scholar 

  5. L. V. Kantorovich, “Some further applications of principle of majorants,” Dokl. Akad. Nauk SSSR, 80, 49–852 (1951).

    Google Scholar 

  6. L. V. Kantorovich, “On approximate solution of functional equations,” Uspekhi Mat. Nauk, 11, 99–116 (1956).

    Google Scholar 

  7. L. V. Kantorovich, “Some further applications of Newton method,” Vestn. LGU, Ser. Math. Mech., No. 7, 8–103 (1957).

  8. L. V. Kantorovich and G. P. Akilov, Functional Analysis in Normed Spaces [in Russian], Fizmatgiz, Moscow (1959).

    Google Scholar 

  9. L. V. Kantorovich and G. P. Akilov, Functional Analysis [in Russian], Nauka, Moscow (1977).

    Google Scholar 

  10. H. Fine, “On Newton's method of approximation,” Proc. Nat. Acad. Sci. USA, 2, 546–552 (1916).

    MATH  Google Scholar 

  11. A. A. Bennet, “Newton's method in general analysis,” Proc. Nat. Acad. Sci. USA, 2, 592–598 (1916).

    Google Scholar 

  12. A. M. Ostrowski, Solution of Equations and Systems of Equations, Academic Press, Basel (1960).

    Google Scholar 

  13. J. M. Ortega and W. C. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables, Academic Press, New York-London (1970).

    Google Scholar 

  14. W. C. Rheinboldt, Methods for Solving Systems of Nonlinear Equations, SIAM, Philadelphia (1998).

    Google Scholar 

  15. P. Deulfhard, Newton Methods for Nonlinear Problems: Affine Invariant and Adaptive Algorithms, Springer Berlin (2004).

    Google Scholar 

  16. T. J. Ypma, “Historical development of the Newton-Raphson method,” SIAM Review, 37, 531–551 (1995).

    Article  MATH  MathSciNet  Google Scholar 

  17. J. H. Mathews, Bibliography for Newton's method; http://math.fullerton.edu/mathews/newtonsmethod/Newton'sMethodBib/Links/Newton'sMethodBib_lnk_3.html.

  18. V. I. Arnold, “Small denominators and problem of stability in classical and celestial mechanics,” Uspekhi Mat. Nauk, 18, 91–192 (1963).

    MATH  Google Scholar 

  19. L. A. Lusternik, “On conditional extrema of functionals,” Mat. Sb., 41, 390–401 (1934).

    MATH  Google Scholar 

  20. A. D. Ioffe, “On the local surjection property,” Nonlinear Anal., 11, 565–592 (1987).

    MATH  MathSciNet  Google Scholar 

  21. I. P. Mysovskikh, “On convergence of L. V. Kantorovich's method for functional equations and its applications,” Dokl. Akad. Nauk SSSR, 70, 565–568 (1950).

    Google Scholar 

  22. M. A. Krasnoselski, G. M. Vainikko, P. P. Zabreiko, Ya. B. Rutitski, and V. Ya. Stetsenko, Approximate Solution of Operator Equations [in Russian], Nauka, Moscow (1969).

    Google Scholar 

  23. L. Collatz, Functional Analysis and Numerical Mathematics, Academic Press, New York (1966).

    Google Scholar 

  24. G. Julia, “Sur l'iteration des fonctions rationelles,” J. Math. Pure Appl., 8, 47–245 (1918).

    MATH  Google Scholar 

  25. M. Barnsley, Fractals Everywhere, Academic Press, London (1993).

    Google Scholar 

  26. B. Mandelbrot, The Fractal Geometry of Nature, W. H. Freeman, New York (1983).

    Google Scholar 

  27. J. H. Curry, L. Garnett, and D. Sullivan, “On the iteration of a rational function: computer experiments with Newton's method,” Comm. Math. Phys., 91, 267–277 (1983).

    Article  MathSciNet  Google Scholar 

  28. H. O. Peitgen, D. Saupe, and F. Haeseler, “Cayley's problem and Julia sets,” Math. Intelligencer, 6, 11–20 (1984).

    MathSciNet  Google Scholar 

  29. D. E. Joyce, “Newton basins”; http://aleph0.clarku.edu/djoyce/newton/newton.html.

  30. R. M. Dickau, “Newton's method”; http://mathforum.org/advanced/robertd/newnewton.html.

  31. K. Levenberg, “A method for the solution of certain nonlinear problems in least squares,” Quart. Appl. Math., 2, 164–168 (1944).

    MATH  MathSciNet  Google Scholar 

  32. D. Marquardt, “An algorithm for least squares estimation of nonlinear parameters,” SIAM J. Appl. Math. 11, 431–441 (1963).

    Article  MATH  MathSciNet  Google Scholar 

  33. S. Goldfeld, R. Quandt, and H. Trotter, “Maximization by quadratic hill climbing,” Econometrica, 34, 41–551 (1966).

    MathSciNet  Google Scholar 

  34. A. B. Conn, N. I. M. Gould, and Ph. L. Toint, Trust Region Methods, SIAM, Philadelphia (2000).

    Google Scholar 

  35. L. M. Graves, “Some mapping theorems,” Duke Math. J., 17, 111–114 (1950).

    Article  MATH  MathSciNet  Google Scholar 

  36. B. T. Polyak, “Gradient methods for solving equations and inequalities,” USSR Comp. Math. Math. Phys. 4, 17–32 (1964).

    MATH  Google Scholar 

  37. B. T. Polyak, “Convexity of nonlinear image of a small ball with applications to optimization,” Set-Valued Anal., 9, 159–168 (2001).

    Article  MATH  MathSciNet  Google Scholar 

  38. B. T. Polyak, “The convexity principle and its applications,” Bull. Braz. Math. Soc., 34, 59–75 (2003).

    MATH  MathSciNet  Google Scholar 

  39. B. T. Polyak, “Convexity of the reachable set of nonlinear systems under L 2 bounded controls,” Dyn. Contin. Discrete Impuls. Syst., 11, 255–268 (2004).

    MATH  MathSciNet  Google Scholar 

  40. Yu. Nesterov and A. Nemirovski, Interior-Point Polynomial Algorithms in Convex Programming, SIAM Philadelphia (1994).

    Google Scholar 

  41. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge Univ. Press, Cambridge (2004).

    Google Scholar 

  42. Yu. Nesterov and B. Polyak, “Cubic regularization of a Newton scheme and its global performance,” CORE Discussion Papers, No. 41 (2003); submitted to Math. Progr.

  43. E. S. Levitin and B. T. Polyak, “Constrained minimization methods,” USSR Comp. Math. Math. Phys., 6, 1–50 (1966).

    Google Scholar 

  44. B. T. Polyak, Introduction to Optimization, Optimization Software, New York (1987).

    Google Scholar 

  45. D. P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont (1999).

    Google Scholar 

  46. B. T. Polyak, “Iterative methods using Lagrange multipliers for solving extremal problems with equality-type constraints,” USSR Comp. Math. Math. Phys., 10, 42–52 (1970).

    Google Scholar 

  47. D. P. Bertsekas, Constrained Optimization and Lagrange Multiplier Method, Academic Press, New York (1982).

    Google Scholar 

  48. A. Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, SIAM, Philadelphia (2001).

    Google Scholar 

  49. Yu. Nesterov, Introductory Lectures on Convex Programming, Kluwer, Boston (2004).

    Google Scholar 

  50. L. T. Biegler and I. E. Grossmann, Part I: “Retrospective on Optimization,” Part II: “Future Perspective on Optimization,” Prepints, Carnegie-Mellon Univ. (2004).

  51. X. Chen, L. Qi, and D. Sun, “Global and superlinear convergence of the smoothing Newton method and its application to general box constrained variational inequalities,” Math. Comp., 67, 519–540 (1998).

    MathSciNet  Google Scholar 

  52. S. Smale, “A convergent process of price adjustment and global Newton methods,” J. Math. Econom., 3, 107–120 (1976).

    MATH  MathSciNet  Google Scholar 

  53. A. G. Ramm, “Acceleration of convergence: a continuous analog of the Newton method,” Appl. Anal., 81 1001–1004 (2002).

    MATH  MathSciNet  Google Scholar 

  54. S. Smale, “Newton's method estimates from data at one point,” in: The Merging of Disciplines: New Directions in Pure, Applied, and Computational Mathematics, R. Ewing, K. Gross, and C. Martin (eds.) Springer (1986).

  55. Electronic library ZIB; http://www.zib.de/SciSoft/NewtonLib.

  56. S. Smale, “Complexity theory and numerical analysis,” Acta Numer., 6, 523–551 (1997).

    MATH  MathSciNet  Google Scholar 

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Published in Zapiski Nauchnykh Seminarov POMI, Vol. 312, 2004, pp. 256–274.

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Polyak, B.T. Newton-Kantorovich Method and Its Global Convergence. J Math Sci 133, 1513–1523 (2006). https://doi.org/10.1007/s10958-006-0066-1

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