An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Authors:
Robert J. Vanderbei;David F. Shanno
Affiliations:
Princeton University. rvdb@princeton.edu;Rutgers University. shanno@tucker.rutgers.edu
Venue:
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Year:
1999

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Abstract

The paper describes an interior-point algorithm for nonconvex nonlinearprogramming which is a direct extension of interior-point methods for linearand quadratic programming. Major modifications include a merit function andan altered search direction to ensure that a descent direction for the meritfunction is obtained. Preliminary numerical testing indicates that themethod is robust. Further, numerical comparisons with MINOS andLANCELOT show that the method is efficient, and has the promise of greatly reducing solution times on at least some classes of models.