A class of algorithms for large scale nonlinear minimax optimization

摘要

Sequential linear programming and sequential quadratic programming based algorithms are often used to solve nonlinear minimax problems. In case of large scale problems, however, these algorithms can be quite tedious, since linear approximations of every nonlinear function are utilized in the mathematical program approximating the original problem (at any iteration). This paper is concerned with algorithms that require, at each iteration, approximations of only a small fraction of the functions. Such methods are thus well suited for large scale problems. Global convergence of this class of algorithms is proven.