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A Primal-Dual Augmented Lagrangian.
Fangyao Su
UCSD
Abstract:
Nonlinear constrained optimization problem can be effectively solved by minimizing a sequence of unconstrained or linearly constrained subproblems, where the augmented Lagrangian function plays a vital role. This talk introduces a generalized Hestenes-Powell augmented Lagrangian function, which can be seen as a continuum of many well-known methods as specific cases. A new primal dual sequential quadratic programming (pdSQP) method will be given for minimizing the given augmented Lagrangian.
Tuesday, May 17, 2016
11:00AM AP&M 2402
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Fangyao Su
UCSD
Abstract:
Nonlinear constrained optimization problem can be effectively solved by minimizing a sequence of unconstrained or linearly constrained subproblems, where the augmented Lagrangian function plays a vital role. This talk introduces a generalized Hestenes-Powell augmented Lagrangian function, which can be seen as a continuum of many well-known methods as specific cases. A new primal dual sequential quadratic programming (pdSQP) method will be given for minimizing the given augmented Lagrangian.
Tuesday, May 17, 2016
11:00AM AP&M 2402