Thursday, May 1, 9:30 am in MTH 3206, University of Maryland,
College Park
On the relaxation of constraints in a particular SQP algorithm
Dr. Tony Kearsley
Mathematical and Computational Sciences Division,
National Institute of Standards and Technology,
Gaithersburg, Maryland
In this talk, we will discuss a sequential quadratic
programming (SQP) algorithm developed to solve general
nonlinear programming (NLP) problems (minimization of a
function subject to non-linear equality and inequality
constraints). With an eye towards a particular class of
application motivated problems, the algorithm solves a
sequence of `relaxed' quadratic programming (QP) problems.
The relaxation guarantees that the linearization of the
constraints yields a consistent set of linear equations
and thus the resulting relaxed QP is solvable. Numerical
performance on a collection of test problems will show
that inconsistent linearizations were encountered by the SQP
algorithm and that the relaxation strategy appears to be
effective at overcoming the difficulty. We conclude the
talk with a short demonstration of the solution to one
particular test problem (optimal control fluid flow).
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