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Derivative-free unconstrained optimization is the class of optimization methods for which the derivatives of the objective function are unavailable. These methods are already well-studied, but are typically restricted to problems involving a small number of variables. The paper discusses how this restriction may be removed by the use the underlying problems structure, both in the case of pattern-search and interpolation methods. The focus is on partially separable objective function, but it is shown how Hessian sparsity, a weaker structure description, can also be used to advantage.
|Number of pages||8|
|Journal||SIOPT Views and News|
|Publication status||Unpublished - 2006|
TOINT, P., COLSON, B., Gratton, S., Tröltzsch, A. & RODRIGUES SAMPAIO, P.
1/03/94 → …
1/01/87 → …
Project: Research Axis