Sequential quadratic programming (SQP) methods are a popular class of
methods for the solution of nonlinear optimization problems. They are
particularly effective for solving a sequence of related problems, such as
those arising in mixed-integer nonlinear programming and the optimization
of functions subject to differential equation constraints.
Recently, there has been considerable interest in the formulation of
stabilized SQP methods, which are specifically designed to give rapid
convergence on degenerate problems. Existing stabilized SQP methods are
essentially local, in the sense that both the formulation and analysis
focus on a neighborhood of an optimal solution. In this talk we discuss an
SQP method that has favorable global convergence properties yet is
equivalent to a conventional stabilized SQP method in the neighborhood of a
solution.

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