In this paper, we give a descent algorithm for solving quadratic bilevel programming problems. It is proved that the descent algorithm finds a locally optimal solution to a quadratic bilevel programming problem in a finite number of iterations. Two numerical examples are given to illustrate this algorithm.
The convergence of the maximum entropy method of nonsmooth semi-infinite programmings is proved, and the stability and the strong stability of the method are discussed.
In this paper, we propose a new smooth function that possesses a property not satisfied by the existing smooth functions. Based on this smooth function, we discuss the existence and continuity of the smoothing path for solving the P0 function nonlinear complementarity problem (NCP). Using the characteristics of the new smooth function, we investigate the boundedness of the iteration sequence generated by the non-interior continuation methods for solving the P0 function NCP under the assumption that the solution set of the NCP is nonempty and bounded. We show that the assumption that the solution set of the NCP is nonempty and bounded is weaker than those required by a few existing continuation methods for solving the NCP.
Two fundamental convergence theorems are given for nonlinear conjugate gradient methods only under the descent condition. As a result, methods related to the Fletcher-Reeves algorithm still converge for parameters in a slightly wider range, in particular, for a parameter in its upper bound. For methods related to the Polak-Ribiere algorithm, it is shown that some negative values of the conjugate parameter do not prevent convergence. If the objective function is convex, some convergence results hold for the Hestenes-Stiefel algorithm.
