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Abstract
Derivativefree unconstrained optimization is the class of optimization methods for which the derivatives of the objective function are unavailable. These methods are already wellstudied, 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 patternsearch 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.
Original language  English 

Pages (fromto)  1118 
Number of pages  8 
Journal  SIOPT Views and News 
Volume  17 
Issue number  1 
Publication status  Unpublished  2006 
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Projects
 2 Active

DFO: Derivative free numerical algorithms for optimization
TOINT, P., COLSON, B., Gratton, S., Tröltzsch, A. & RODRIGUES SAMPAIO, P.
1/03/94 → …
Project: Research
