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We consider a class of nonlinear optimization problems with "expensive" objective function and constraints. For such problems the derivatives of the objective function and, possibly, constraints are assumed unavailable. Also some noise may be present in the function computations. We will describe a class of, so called, derivative free optimization (DFO) algorithms developed for constrained and unconstrained problems.
This is joint work with Katya Scheinberg and Phillipe Toint.
Below, you can download a compressed whole package or separate postscript files of the materials used during the presentation.
|aconn.tar.gz (complete version, 1.8meg)||ima.ps (249K)||dfo1b.eps (2.5meg)||dfo2b.eps (2.5meg)||dfo3b.eps (2.5meg)||dfo4b.eps (2.5meg)||refs.ps (46K)|
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