caa a22 4500 475769406 CHVBK 20180406123615.0 cr unu---uuuuu 170329e20000401xx s 000 0 eng 10.1007/s101070050116 doi (NATIONALLICENCE)springer-10.1007/s101070050116 Interior-point methods for nonconvex nonlinear programming: orderings and higher-order methods [Elektronische Daten] [David F. Shanno, Robert J. Vanderbei] Abstract. : The paper extends prior work by the authors on loqo, an interior point algorithm for nonconvex nonlinear programming. The specific topics covered include primal versus dual orderings and higher order methods, which attempt to use each factorization of the Hessian matrix more than once to improve computational efficiency. Results show that unlike linear and convex quadratic programming, higher order corrections to the central trajectory are not useful for nonconvex nonlinear programming, but that a variant of Mehrotra's predictor-corrector algorithm can definitely improve performance. Springer-Verlag Berlin Heidelberg, 2000 Key words: nonlinear programming - interior-point methods - nonconvex optimization - predictor-corrector - matrix ordering nationallicence Shanno David F. Rutgers University, New Brunswick, NJ, USA, US aut Vanderbei Robert J. Princeton University, Princeton, NJ, USA, US aut https://doi.org/10.1007/s101070050116 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s101070050116 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shanno David F. Rutgers University, New Brunswick, NJ, USA, US aut NATIONALLICENCE 700 1- Vanderbei Robert J. Princeton University, Princeton, NJ, USA, US aut Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer