3:30 PM-5:30 PM
Room: Atlanta 1 and 2
Minimization problems without gradient information are not rare. Functions that require experiments, either real or computational, table lookup, or black-box internal computations are examples. The speakers in this minisymposium will consider a variety of methods for coping with this difficulty. Direct search methods, methods which build models from the history of the optimization, and methods that use finite differences to find whatever gradient information is in the function will be considered. The talks in this minisymposium will address some of the possibilities for minimization in the absence of explicit gradient information
Organizer: C. T. Kelley
North Carolina State University
MMD, 12/21/98