Abstract
The regularized Newton method (RNM) is one of the efficient solution methods for the unconstrained convex optimization. It is well-known that the RNM has good convergence properties as compared to the steepest descent method and the pure Newton’s method. For example, Li, Fukushima, Qi and Yamashita showed that the RNM has a quadratic rate of convergence under the local error bound condition. Recently, Polyak showed that the global complexity bound of the RNM, which is the first iteration k such that ‖∇ f(x k )‖≤ε, is O(ε −4), where f is the objective function and ε is a given positive constant. In this paper, we consider a RNM extended to the unconstrained “nonconvex” optimization. We show that the extended RNM (E-RNM) has the following properties. (a) The E-RNM has a global convergence property under appropriate conditions. (b) The global complexity bound of the E-RNM is O(ε −2) if ∇ 2 f is Lipschitz continuous on a certain compact set. (c) The E-RNM has a superlinear rate of convergence under the local error bound condition.
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Dan, H., Yamashita, N., Fukushima, M.: Convergence properties of the inexact Levenberg-Marquardt method under local error bound conditions. Optim. Methods Softw. 17, 605–626 (2002)
Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (1985)
Li, Y.J., Li, D.H.: Truncated regularized Newton method for convex minimizations. Comput. Optim. Appl. 43, 119–131 (2009)
Li, D.H., Fukushima, M., Qil, L., Yamashita, N.: Regularized Newton methods for convex minimization problems with singular solutions. Comput. Optim. Appl. 28, 131–147 (2004)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999)
Polyak, R.A.: Regularized Newton method for unconstrained convex optimization. Math. Program., Ser. B 120, 125–145 (2009)
Yamashita, N., Fukushima, M.: On the rate of convergence of the Levenberg-Marguardt method. Comput., Suppl (Wien) 15, 227–238 (2001)
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Ueda, K., Yamashita, N. Convergence Properties of the Regularized Newton Method for the Unconstrained Nonconvex Optimization. Appl Math Optim 62, 27–46 (2010). https://doi.org/10.1007/s00245-009-9094-9
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DOI: https://doi.org/10.1007/s00245-009-9094-9