An Interior--Point Algorithm for Nonconvex Nonlinear
Robert J. Vanderbei and David. F. Shanno
The paper describes an interior--point algorithm for nonconvex
nonlinear programming which is a direct extension of interior--point
methods for linear and quadratic programming. Major modifications
include a merit function and an altered search direction to ensure
that a descent direction for the merit function is obtained.
Preliminary numerical testing indicates that the method is robust.
Further, numerical comparisons with minos and lancelot
show that the method is efficient, and has the promise of greatly
reducing solution times on at least some classes of models.
SOR 97-21, Princeton University, May 1998 (REVISED)