Please use this identifier to cite or link to this item: https://doi.org/10.1137/S1052623402400641
Title: A robust primal-dual interior-point algorithm for nonlinear programs
Authors: Liu, X. 
Sun, J. 
Keywords: Global convergence
Interior-point
Method
Nonlinear optimization
Regularity conditions
Issue Date: 2004
Citation: Liu, X., Sun, J. (2004). A robust primal-dual interior-point algorithm for nonlinear programs. SIAM Journal on Optimization 14 (4) : 1163-1186. ScholarBank@NUS Repository. https://doi.org/10.1137/S1052623402400641
Abstract: We present a primal-dual interior-point algorithm for solving optimization problems with nonlinear inequality constraints. The algorithm has some of the theoretical properties of trust region methods, but works entirely by line search. Global convergence properties are derived without assuming regularity conditions. The penalty parameter p in the merit function is updated adaptively and plays two roles in the algorithm. First, it guarantees that the search directions are descent directions of the updated merit function. Second, it helps to determine a suitable search direction in a decomposed SQP step. It is shown that if ρ is bounded for each barrier parameter μ, then every limit point of the sequence generated by the algorithm is a Karush Kuhn-Tucker point, whereas if ρ is unbounded for some μ, then the sequence has a limit point which is either a Fritz-John point or a stationary point of a function measuring the violation of the constraints. Numerical results confirm that the algorithm produces the correct results for some hard problems, including the example provided by Wächter and Biegler, for which many of the existing line search-based interior-point methods have failed to find the right answers.
Source Title: SIAM Journal on Optimization
URI: http://scholarbank.nus.edu.sg/handle/10635/44231
ISSN: 10526234
DOI: 10.1137/S1052623402400641
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