On the evaluation complexity of composite function minimization with applications to nonconvex nonlinear programming

C Cartis (coralia.cartised.ac.uk)
N I M Gould (nick.gouldstfc.ac.uk)
Ph L Toint (philippe.tointfundp.ac.be)

Abstract: We estimate the worst-case complexity of minimizing an unconstrained, nonconvex composite objective with a structured nonsmooth term by means of some first-order methods. We find that it is unaffected by the nonsmoothness of the objective in that a first-order trust-region or quadratic regularization method applied to it takes at most O($\epsilon^{-2}$) function-evaluations to reduce the size of a first-order criticality measure below $\epsilon$. Specializing this result to the case when the composite objective is an exact penalty function allows us to consider the objective- and constraint-evaluation worst-case complexity of nonconvex equality-constrained optimization when the solution is computed using a first-order exact penalty method. We obtain that in the reasonable case when the penalty parameters are bounded, the complexity of reaching within $\epsilon$ of a KKT point is at most O($\epsilon^{-2}$) problem-evaluations, which is the same in order as the function-evaluation complexity of steepest-descent methods applied to unconstrained, nonconvex smooth optimization.

Keywords: steepest descent method, trust region method, global rate of convergence, nonlinear programming

Category 1: Nonlinear Optimization

Citation: ERGO Technical Report 11-002, School of Mathematics, University of Edinburgh, UK, 2011.

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Entry Submitted: 02/08/2011
Entry Accepted: 02/08/2011
Entry Last Modified: 05/01/2011

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