On the Number of Inner Iterations Per Outer Iteration of a Globally Convergent Algorithm for Optimization with General Nonlinear Inequality Constraints and Simple Bounds

Authors:
A. R. Conn;N. Gould;Ph. L. Toint
Affiliations:
IBM T.J. Watson Research Center, Yorktown Heights, USA/ E-mail: arconn@watson.ibm.com;Rutherford Appleton Laboratory, Chilton, Oxfordshire, England/ E-mail: N.Gould@letterbox.rl.ac.uk;Department of Mathematics, Faculté/s Universitaires ND de la Paix, Namur, Belgium/ E-mail: pht@math.fundp.ac.be
Venue:
Computational Optimization and Applications
Year:
1997

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Abstract

This paper considers the number of inner iterations required per outeriteration for the algorithm proposed by Conn et al.[9]. We show that asymptotically, under suitable reasonable assumptions, a single inner iteration suffices.