Armijo-type condition for the determination of a generalized Cauchy point in trust region algorithms using exact or inexact projections on convex constraints

    Research output: Contribution to journalArticle

    445 Downloads (Pure)

    Abstract

    This paper considers some aspects of two classes of trust region methods for solving constrained optimization problems. The first class proposed by Toint uses techniques based on the explicitly calculated projected gradient while the second class proposed by Conn, Gould, Sartenaer and Toint allows for inexact projections on the constraints. We propose and analyze, for each class, a step-size rule in the spirit of the Armijo rule for the determination of a Generalized Cauchy Point. We then prove under mild assumptions that, in both cases, the classes preserve their theoretical properties of global convergence and identification of the correct active set in a finite number of iterations. Numerical issues are also discussed for both classes.
    Original languageEnglish
    Pages (from-to)61-75
    Number of pages15
    JournalBelgian Journal of Operations Research, Statistics and Computer Science
    Volume33
    Issue number4
    Publication statusPublished - 1993

    Fingerprint Dive into the research topics of 'Armijo-type condition for the determination of a generalized Cauchy point in trust region algorithms using exact or inexact projections on convex constraints'. Together they form a unique fingerprint.

    Cite this