Optimization using surrogates for engineering design
by John Dennis
Abstract: This talk will outline the surrogate management framework,
which is presently built on the filter MADS
method for general nonlinear programming without derivatives.
The focus is on the numerical results, with a brief introduction to
the MADS algorithm and a slight mention of the convergence
This line of research was motivated by
industrial applications, indeed, by a question I was asked by Paul
Frank of Boeing Phantom Works. His group was often asked for help
in dealing with very low dimensional design problems driven by
expensive simulations. Everyone there was dissatisfied with
the common practice of substituting
inexpensive surrogates for the expensive ``true'' objective and
constraint functions in the optimal design formulation.
I hope to demonstrate in this
talk just how simple the answer to Paul's question is.
The surrogate management framework has been implemented successfully
by several different groups, and it is unreasonably effective in
practice, where most of the application are extended valued and
certainly nondifferentiable. This has forced my colleagues and me
to begin to learn some nonsmooth analysis, which in turn has led to MADS, a replacement for the GPS infrastructure algorithm.
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