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A path-following primal-dual augmented Lagrangian method for NEP
Fangyao Su
UCSD
Abstract:
A new path-following primal-dual augmented Lagrangian method is proposed for solving nonlinear equality constrained optimization problems (NEP). At each iteration, a Newton-like method is used to solve a perturbed optimality condition that defines a penalty trajectory parameterized by both the penalty parameter and the estimated Lagrange multipliers. We show that this method is globally convergent and has a quadratic convergence rate in the limit. Finally, numerical experiments on problems from the CUTEst test collection are are used to support the theoretical analysis.
Tuesday, May 15, 2018
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:
A new path-following primal-dual augmented Lagrangian method is proposed for solving nonlinear equality constrained optimization problems (NEP). At each iteration, a Newton-like method is used to solve a perturbed optimality condition that defines a penalty trajectory parameterized by both the penalty parameter and the estimated Lagrange multipliers. We show that this method is globally convergent and has a quadratic convergence rate in the limit. Finally, numerical experiments on problems from the CUTEst test collection are are used to support the theoretical analysis.
Tuesday, May 15, 2018
11:00AM AP&M 2402