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

Andy Conn; Nick Gould; Philippe Toint

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

Andy Conn, Nick Gould, Philippe Toint

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Abstract

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

Original language	English
Pages (from-to)	41-69
Number of pages	29
Journal	Computational Optimization and Applications
Volume	7
Issue number	1
Publication status	Published - 1 Jan 1997

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

Abstract

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ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

LANCELOT: LANCELOT, a package for the solution of large-scale nonlinear optimization problems

Cite this

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

Abstract

Access to Document

Other files and links

Fingerprint

Projects

ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

LANCELOT: LANCELOT, a package for the solution of large-scale nonlinear optimization problems

Cite this