In this paper we present a method for using rational expectations in a linearquadratic optimization framework with learning. We present a method that allows a policy maker to derive an optimal policy in the presence of rational expectations and the possibility of parameter drift. In this fashion the Lucas critique can be mitigated.

This manuscript introduces ANTIGONE, Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations, a general mixed-integer nonlinear global optimization framework. ANTIGONE is the evolution of the Global Mixed-Integer Quadratic Optimizer, GloMIQO, to general nonconvex terms. The purpose of this paper is to show how the extensible structure of ANTIGONE realizes our previously-p...

A Real-Time Optimization (RTO) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. Furthermore, the objective function is penalized by a fuzzified form of the key process constraints. To enable using conventional optimization techniques, the resulting fuzzy optimization problem is the...

In this paper we discuss problems with quadratic objective function, one or two quadratic constraints, and, possibly, some additional linear constraints. In particular, we consider cases where the Hessian of the quadratic functions are simultaneously diagonalizable, so that the objective and constraint functions can all be converted into separable functions. We give conditions under which a sim...

We consider the nonconvex problem minimizing the ratio of two quadratic functions over finitely many nonconvex quadratic inequalities. Relying on the homogenization technique we establish a sufficient condition that warrants the attainment of an optimal solution. Our result allows to extend and recover known conditions for some interesting special instances of the problem and to derive further ...