Skip to main content
Log in

Globally Convergent, Iterative Path-Following for Algebraic Equations

  • Published:
Mathematics in Computer Science Aims and scope Submit manuscript

Abstract

Homotopy methods are of great importance for the solution of systems of equations. It is a major problem to ensure well-defined iterations along the homotopy path. Many investigations have considered the complexity of path-following methods depending on the unknown distance of some given path to the variety of ill-posed problems. It is shown here that there exists a construction method for safe paths for a single algebraic equation. A safe path may be effectively determined with bounded effort. Special perturbation estimates for the zeros together with convergence conditions for Newton’s method in simultaneous mode allow our method to proceed on the safe path. This yields the first globally convergent, never-failing, uniformly iterative path-following algorithm. The maximum number of homotopy steps in our algorithm reaches a theoretical bound forecast by Shub and Smale i.e., the number of steps is at most quadratic in the condition number. A constructive proof of the fundamental theorem of algebra meeting demands by Gauß, Kronecker and Weierstraß is a consequence of our algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Batra P.: Simultaneous point estimates for Newton’s method. BIT Numer. Math. 42(3), 467–476 (2002)

    MathSciNet  MATH  Google Scholar 

  2. Batra P.: Newton’s method and the computational complexity of the fundamental theorem of algebra. ENTCS 202, 201–218 (2008)

    MathSciNet  Google Scholar 

  3. Beauzamy B.: How the roots of a polynomial vary with its coefficients: a local quantitative result. Can. Math. Bull. 42(1), 3–12 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  4. Beltrán C., Pardo L.M.: On Smale’s 17th problem: a probabilistic positive solution. Found. Comp. Math. 8(1), 1–43 (2008)

    Article  MATH  Google Scholar 

  5. Blum L., Cucker F., Shub M., Smale S.: Complexity and Real Computation. Springer, New York (1998)

    Google Scholar 

  6. Bürgisser, P.: Smoothed analysis of condition numbers. In: Proceedings of the ICM 2010, Hyderabad, India, August 2010. IMU, Berlin (2010). http://www-math.uni-paderborn.de/agpb/work/pbuerg-icm2010.pdf

  7. Bürgisser, P., Cucker, F.: On a problem posed by Steve Smale. In: STOC’10: Proceedings of the 42nd Annual ACM Symposium on Theory of Computing, Cambridge, Mass., USA; June 2010. ACM (2010). http://www-math.uni-paderborn.de/agpb/work/0909.2114v4.pdf

  8. Collins G.E., Horowitz E.: The minimum root separation of a polynomial. Math. Comput. 28(126), 589–597 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  9. Cucker F., Blum L.: The work of Steve Smale on the theory of computation: 1990–1999. In: Cucker, F., Rojas, J.M. (eds) Foundations of Computational Mathematics, pp. 15–33. World Scientific, Singapore (2002)

    Google Scholar 

  10. Ćurgus B., Mascioni V.: On the location of critical points of polynomials. Proc. Am. Math. Soc. 131(1), 253–264 (2003)

    Article  MATH  Google Scholar 

  11. Darboux, G.: Sur les développements en série des fonctions d’une seule variable. Journal de mathématiques pures et appliquées (Liouville Journal)(3) II:291–312 (1876). http://gallica.bnf.fr/ark:/12148/bpt6k16420b.image.f291

  12. Dedieu J.-P.: Points fixes, zéros et la méthode de Newton. Springer, Berlin (2006)

    MATH  Google Scholar 

  13. Demmel J.W.: On condition numbers and the distance to the nearest ill-posed problem. Numer. Math. 51, 251–289 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  14. Doyle P., McMullen C.: Solving the quintic by iteration. Acta Math. 163, 151–180 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  15. Edwards H.M.: Essays in Constructive Mathematics. Springer, New York (2005)

    MATH  Google Scholar 

  16. Edwards H.M.: Kronecker’s algorithmic mathematics. Math. Intell. 31(2), 11–14 (2009)

    Article  MATH  Google Scholar 

  17. Galántai A., Hegedűs C.J.: Perturbation bounds for polynomials. Numer. Math. 109(1), 77–100 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Giusti M., Lecerf G., Salvy B., Yakoubsohn J.-C.: On location and approximation of clusters of zeros of analytic functions. Found. Comp. Math. 5(3), 257–311 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  19. Goldstein A.J., Graham R.L.: A Hadamard-type bound on the coefficients of a determinant of polynomials. SIAM Rev. 16, 394–395 (1974)

    Article  Google Scholar 

  20. Grenander, U., Szegö, G.: Toeplitz Forms and their Applications. Chelsea, New York (Reprint of 1st ed. from 1958) (1984)

  21. Hirsch M.W., Smale S.: On algorithms for solving f(x) = 0. Commun. Pure Appl. Math. XXXII, 281–312 (1979)

    Article  MathSciNet  Google Scholar 

  22. Hubbard J., Schleicher D., Sutherland S.: How to find all roots of complex polynomials by Newton’s method. Invent. Math. 146(1), 1–33 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  23. Kim, M.H.: Computational complexity of the Euler-type algorithms for the roots of complex polynomials. PhD thesis, City University of New York (1985)

  24. Kim M.-H., Sutherland S.: Polynomial root-finding algorithms and branched covers. SIAM J. Comput. 23(2), 415–436 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  25. Kirrinnis P.: Partial fraction decomposition in \({\mathbb{C}(z)}\) and simultaneous Newton iteration for factorization in \({\mathbb{C}[z]}\) . J. Complexity 14, 378–444 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  26. Kronecker L.: Grundzüge einer arithmetischen Theorie der algebraischen Größen. Journal f. d. reine und angewandte Mathematik 92, 1–122 (1882)

    Google Scholar 

  27. Malajovich G.: On generalized Newton algorithms: quadratic convergence, path-following and error analysis. Theor. Comput. Sci. 133, 65–84 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  28. Marden M.M.: The Geometry of Polynomials, 2nd ed. AMS, Providence (1966)

    Google Scholar 

  29. McMullen C.: Families of rational maps and iterative root-finding algorithms. Ann. Math. 125, 467–493 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  30. Mora T.: Solving Polynomial Equation Systems I. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  31. Neff C.A., Reif J.H.: An efficient algorithm for the complex roots problem. J. Complexity 12, 81–115 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  32. Netto, E.: Die vier Gauss’schen Beweise für die Zerlegung ganzer algebraischer Funktionen in reelle Factoren ersten oder zweiten Grades (1799–1849). Akademische Verlagsgesellschaft m.b.H. in Leipzig; Verlag von Wilhelm Engelmann, Leipzig und Berlin, 3rd edition (1913)

  33. Ostrowski A.: Solution of Equations in Euclidean and Banach Spaces. Academic Press, New York (1973)

    MATH  Google Scholar 

  34. Pan V.Y.: Solving a polynomial equation: some history and recent progress. SIAM Rev. 39(2), 187–220 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  35. Rahman Q.I., Schmeisser G.: Analytic Theory of Polynomials. Oxford University Press, Oxford (2002)

    MATH  Google Scholar 

  36. Rosenbloom P.C.: Perturbation of zeros of analytic functions. II. J. Approx. Theory 2, 275–300 (1969)

    Article  MathSciNet  MATH  Google Scholar 

  37. Schönhage, A.: The fundamental theorem of algebra in terms of computational complexity. Preliminary report. http://www.informatik.uni-bonn.de/~schoe/fdthmrep.ps.gz, August 1982

  38. Sharma, V.: Complexity analysis of algorithms in algebraic computation. PhD thesis, Courant Institute of Mathematical Sciences, New York University (2007)

  39. Sharma, V., Du, Z., Yap, C.K.: Robust approximate zeros. In: Brodal, G.S. (ed.) Algorithms–ESA 2005. Lecture Notes in Computer Science, vol. 3669, pp. 874–886. Springer, Berlin (2005)

  40. Shub M., Smale S.: Computational complexity: on the geometry of polynomials and a theory of cost: II. SIAM J. Comput. 15(1), 145–161 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  41. Shub M., Smale S.: Complexity of Bezout’s theorem. I: geometric aspects. J. AMS 6(2), 459–501 (1993)

    MathSciNet  MATH  Google Scholar 

  42. Shub M., Smale S.: Complexity of Bezout’s theorem. V: polynomial time. Theor. Comput. Sci. 133, 141–164 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  43. Shub M., Smale S.: Complexity of Bezout’s theorem. IV: probability of success; extensions. SIAM J. Numer. Anal. 33(1), 128–148 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  44. Smale S.: On the efficiency of algorithms of analysis. Bull. AMS (N.S.) 13(2), 87–121 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  45. Smale, S.: Algorithms for solving equations. In: Proceedings of the International Congress of Mathematicians, Berkeley, CA, USA, pp. 172–195. AMS, Providence (1986)

  46. Smale S.: Newton’s method estimates from data at one point. In: Ewing, R.E., Gross, K.I., Martin, C.F. (eds) The Merging of Disciplines, pp. 185–196. Springer, New York (1986)

    Chapter  Google Scholar 

  47. Smale S.: Complexity theory and numerical analysis. Acta Numer. 6, 523–551 (1997)

    Article  MathSciNet  Google Scholar 

  48. Smale S.: Mathematical problems for the next century. In: Arnold, V. (eds) Mathematics: Frontiers and Perspectives, pp. 271–294. AMS, Providence (2000)

    Google Scholar 

  49. Wang X.: A summary on continuous complexity theory. Contemp. Math. 163, 155–170 (1994)

    Google Scholar 

  50. Wang X.-H., Han D.-F.: On dominating sequence method in the point estimate and Smale theorem. Sci. China 33(2), 135–144 (1990)

    MathSciNet  MATH  Google Scholar 

  51. Wang X., Shen G., Han D.: Some remarks on Smale’s “Algorithms for solving equations”. Acta Math. Sin., New Series 8(4), 337–348 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  52. Weierstraß, K.: Neuer Beweis des Satzes, dass jede ganze rationale Function einer Veränderlichen dargestellt werden kann als ein Product aus linearen Functionen derselben Veränderlichen. Sitzungsberichte der Akademie der Wissenschaften zu Berlin, pp. 1085–1101, 1891. In: Mathematische Werke, volume III, pp. 251–269. Mayer & Müller, Berlin, 1903. http://ia310831.us.archive.org/3/items/mathematischewer03weieuoft/mathematischewer03weieuoft.pdf

  53. Weyl H.: Randbemerkungen zu Hauptproblemen der Mathematik. Math. Zeitschrift 20, 131–150 (1924)

    Article  MathSciNet  Google Scholar 

  54. Whittaker, E.T., Watson, G.N.: Modern Analysis, 4th ed., 1927. Cambridge University Press, Cambridge. Reprinted 1980

  55. Yakoubsohn J.-C.: Simultaneous computation of all the zero-clusters of a univariate polynomial. In: Cucker, F., Rojas, J.M. (eds) Foundations of Computational Mathematics, pp. 433–455. World Scientific, Singapore (2002)

    Google Scholar 

  56. Yap C.K.: Fundamental Problems of Algorithmic Algebra. Oxford University Press, New York (2000)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prashant Batra.

Additional information

To the memory of Y. L. Batra.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Batra, P. Globally Convergent, Iterative Path-Following for Algebraic Equations. Math.Comput.Sci. 4, 507 (2010). https://doi.org/10.1007/s11786-011-0069-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11786-011-0069-2

Keywords

Navigation