On the optimal order of worst case complexity of direct search

• Autor(a) principal: Dodangeh, Mahdi
• Data de Publicação: 2016
• Outros Autores: Vicente, Luís Nunes, Zhang, Zaikun
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/45237; https://doi.org/10.1007/s11590-015-0908-1

The worst case complexity of direct-search methods has been recently analyzed when they use positive spanning sets and impose a sufficient decrease condition to accept new iterates. For smooth unconstrained optimization, it is now known that such methods require at most \mathcal {O}(n^2\epsilon ^{-2}) function evaluations to compute a gradient of norm below \epsilon \in (0,1), where n is the dimension of the problem. Such a maximal effort is reduced to \mathcal {O}(n^2\epsilon ^{-1}) if the function is convex. The factor n^2 has been derived using the positive spanning set formed by the coordinate vectors and their negatives at all iterations. In this paper, we prove that such a factor of n^2 is optimal in these worst case complexity bounds, in the sense that no other positive spanning set will yield a better order of n. The proof is based on an observation that reveals the connection between cosine measure in positive spanning and sphere covering.