On the evaluation complexity of constrained nonlinear least-squares and general constrained nonlinear optimization using second-order methods

Coralia Cartis, Nicholas I M Gould, Philippe L. Toint

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Résumé

When solving the general smooth nonlinear and possibly nonconvex optimization problem involving equality and/or inequality constraints, an approximate first-order critical point of accuracy ∈ can be obtained by a second-order method using cubic regularization in at most O(∈<sup>-3/2</sup> ) evaluations of problem functions, the same order bound as in the unconstrained case. This result is obtained by first showing that the same result holds for inequality constrained nonlinear least-squares. As a consequence, the presence of (possibly nonconvex) equality/inequality constraints does not affect the complexity of finding approximate first-order critical points in nonconvex optimization. This result improves on the best known (O(∈<sup>-2</sup> )) evaluation-complexity bound for solving general nonconvexly constrained optimization problems.

langue originaleAnglais
Pages (de - à)836-851
Nombre de pages16
journalSIAM Journal on Numerical Analysis
Volume53
Numéro de publication2
Les DOIs
Etat de la publicationPublié - 2015

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