A globally convergent primal-dual interior-point filter method for nonlinear programming: new filter optimality measures and computational results

R. Silva(renatamat.uc.pt)
M. Ulbrich(mulbrichma.tum.de)
S. Ulbrich(ulbrichmathematik.tu-darmstadt.de)
L. N. Vicente(lnvmat.uc.pt)

Abstract: In this paper we modify the original primal-dual interior-point filter method proposed in [18] for the solution of nonlinear programming problems. We introduce two new optimality filter entries based on the objective function, and thus better suited for the purposes of minimization, and propose conditions for using inexact Hessians. We show that the global convergence properties of the method remain true under such modifications. We also introduce a new optimization solver for the solution of nonlinear programming problems, called ipfilter, based on our primal-dual interior-point filter approach. The numerical results reported show that ipfilter is competitive both in efficiency and robustness and can handle large instances.

Keywords: interior-point methods, primal-dual, filter, global convergence, large-scale NLP

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Category 2: Nonlinear Optimization (Quadratic Programming )

Category 3: Optimization Software and Modeling Systems

Citation: Preprint 08-49, Dept. Mathematics, Univ. Coimbra

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Entry Submitted: 10/02/2008
Entry Accepted: 10/02/2008
Entry Last Modified: 10/02/2008

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