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A Primal-Dual Augmented Lagrangian Penalty-Interior-Point Filter Line Search Algorithm

Renke Kuhlmann (renke.kuhlmann***at***math.uni-bremen.de)
Christof Büskens (bueskens***at***math.uni-bremen.de)

Abstract: Interior-point methods have been shown to be very efficient for large-scale nonlinear programming. The combination with penalty methods increases their robustness due to the regularization of the constraints caused by the penalty term. In this paper a primal-dual penalty-interior-point algorithm is proposed, that is based on an augmented Lagrangian approach with an l2-exact penalty function. Global convergence is maintained by a combination of a merit function and a filter approach. Unlike other filter methods no separate feasibility restoration phase is required. The algorithm has been implemented within the solver WORHP to study different penalty and line search options and to compare its numerical performance to two other state-of-the-art nonlinear programming algorithms, the interior-point method IPOPT and the sequential quadratic programming method of WORHP.

Keywords: Nonlinear Programming, Constrained Optimization, Augmented Lagrangian, Penalty-Interior-Point Algorithm, Primal-Dual Method

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )



Entry Submitted: 05/11/2017
Entry Accepted: 05/11/2017
Entry Last Modified: 12/22/2017

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