##### Department of Mathematics,

University of California San Diego

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### Center for Computational Mathematics Seminar

## Fangyao Su

#### UCSD

## A path-following primal-dual augmented Lagrangian method for NEP

##### 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.

### May 15, 2018

### 11:00 AM

### AP&M 2402

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