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On the complexity of finding first-order critical points in constrained nonlinear optimization

Coralia Cartis(coralia.cartis***at***ed.ac.uk)
Nicholas I. M. Gould( nick.gould***at***sftc.ac.uk)
Philippe L. Toint(philippe.toint***at***fundp.ac.be)

Abstract: The complexity of finding epsilon-approximate first-order critical points for the general smooth constrained optimization problem is shown to be no worse that O(epsilon^{-2}) in terms of function and constraints evaluations. This result is obtained by analyzing the worst-case behaviour of a first-order shorts-step homotopy algorithm consisting of a feasibility phase followed by an optimization phase, and requires minimal assumptions on the objective function. Since a bound of the same order is known to be valid for the unconstrained case, this leads to the conclusion that the presence of possibly nonlinear/nonconvex inequality/equality constraints is irrelevant for this bound to apply.

Keywords: evaluation complexity, worst-case analysis, constrained nonlinear optimization

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: naXys Report 13-2011, Namur Center for Complex Systems (naXys), University of Namur, Namur (Belgium)

Download: [PDF]

Entry Submitted: 04/14/2011
Entry Accepted: 04/14/2011
Entry Last Modified: 04/14/2011

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