Optimality-Based Discretization Methods for the Global Optimization of Nonconvex Semi-Infinite Programs

We use sensitivity analysis to design optimality-based discretization (cutting-plane) methods for the global optimization of nonconvex semi-infinite programs (SIPs). We begin by formulating the optimal discretization of SIPs as a max-min problem and propose variants that are more computationally tractable. We then use parametric sensitivity theory to design an efficient method for solving these max-min … Read more

Pathfollowing for Parametric Mathematical Programs with Complementarity Constraints

In this paper we study procedures for pathfollowing parametric mathematical pro- grams with complementarity constraints. We present two procedures, one based on the penalty approach to solving standalone MPCCs, and one based on tracing active set bifurcations aris- ing from doubly-active complementarity constraints. We demonstrate the performance of these approaches on a variety of examples … Read more

A predictor-corrector path-following algorithm for dual-degenerate parametric optimization problems

Most path-following algorithms for tracing a solution path of a parametric nonlinear optimization problem are only certifiably convergent under strong regularity assumptions about the problem functions, in particular, the linear independence of the constraint gradients at the solutions, which implies a unique multiplier solution for every nonlinear program. In this paper we propose and prove … Read more