What's new in SQP methods?

Wednesday, November 19, 2008 - 9:00am - 10:00am
EE/CS 3-180
Philip Gill (University of California, San Diego)
Sequential quadratic programming (SQP)
methods a powerful and effective class of methods for a
wide range of nonlinearly constrained optimization
problems. Given the scope and utility of nonlinear
optimization, it is not surprising that SQP methods are
still a subject of active research. Recent
developments in methods for mixed integer nonlinear
programming and the minimization of functions subject
to differential equation constraints has led to a
heightened interest in methods that may be hot
started from a good approximate solution. We discuss
the role of SQP methods in this context, with
particular reference to some recent enhancements to our
large-scale SQP package SNOPT. We end with some
discussion of the challenges associated with
formulating algorithms that can exploit multicore and
GPU-based computer architectures.
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