A new family of Newton-type iterative methods with and without memory for solving nonlinear equations

  • Authors:

  • Xiaofeng Wang;Tie Zhang

  • Affiliations:

  • School of Mathematics and Physics, Bohai University, Jinzhou, China 121013 and College of Sciences, Northeastern University, Shenyang, China 110819;College of Sciences, Northeastern University, Shenyang, China 110819

  • Venue:
  • Calcolo: a quarterly on numerical analysis and theory of computation
  • Year:
  • 2014

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Abstract

In this paper, we present a new family of two-step iterative methods for solving nonlinear equations. The order of convergence of the new family without memory is four requiring three functional evaluations, which implies that this family is optimal according to Kung and Traubs conjecture Kung and Traub (J Appl Comput Math 21:643---651, 1974). Further accelerations of convergence speed are obtained by varying a free parameter in per full iteration. This self-accelerating parameter is calculated by using information available from the current and previous iteration. The corresponding R-order of convergence is increased form 4 to $$\frac{5+\sqrt{17}}{2}\approx 4.5616, \frac{5+\sqrt{21}}{2}\approx 4.7913$$ and 5. The increase of convergence order is attained without any additional calculations so that the family of the methods with memory possesses a very high computational efficiency. Another advantage of the new methods is that they remove the severe condition $$f^{\prime }(x)$$ in a neighborhood of the required root imposed on Newtons method. Numerical comparisons are made to show the performance of our methods, as shown in the illustration examples.