An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity

Coralia Cartis, Nick Gould, Philippe Toint

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Abstract

The adaptive cubic regularization algorithm described in Cartis et al. (2009, Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results. Math. Program., 127, 245-295; 2010, Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity [online]. Math. Program., DOI: 10.1007/s10107-009-0337-y) is adapted to the problem of minimizing a nonlinear, possibly nonconvex, smooth objective function over a convex domain. Convergence to first-order critical points is shown under standard assumptions, without any Lipschitz continuity requirement on the objective's Hessian. A worst-case complexity analysis in terms of evaluations of the problem's function and derivatives is also presented for the Lipschitz continuous case and for a variant of the resulting algorithm. This analysis extends the best-known bound for general unconstrained problems to nonlinear problems with convex constraints. © 2012 Crown copyright 2012. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
Original languageEnglish
Pages (from-to)1662-1695
Number of pages34
JournalIMA Journal of Numerical Analysis
Volume32
Issue number4
DOIs
Publication statusPublished - 1 Oct 2012

Keywords

  • global convergence
  • convex constraints
  • numerical algorithms
  • cubic regularisation
  • worst-case complexity.
  • Nonlinear optimization

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  • Projects

    Complexity in nonlinear optimization

    TOINT, P., Gould, N. I. M. & Cartis, C.

    1/11/08 → …

    Project: Research

    Activities

    • 4 Oral presentation
    • 1 Research/Teaching in a external institution
    • 1 Visiting an external academic institution

    How much patience do you have? Issues in complexity for nonlinear optimization

    Philippe Toint (Invited speaker)

    5 Feb 2016

    Activity: Talk or presentation typesOral presentation

    Polytechnic University of Hong Kong

    Philippe Toint (Visiting researcher)

    31 Jan 201614 Feb 2016

    Activity: Visiting an external institution typesResearch/Teaching in a external institution

    How much patience do you have? Issues in complexity for nonlinear optimization

    Philippe Toint (Speaker)

    31 Jan 2016

    Activity: Talk or presentation typesOral presentation

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