Changjun Yu

TL;DR: In this article, a computational approach based on a new exact penalty refinement method is devised for solving a class of continuous======INEquality constrained optimization problems, where the continuous======inequality constraints are first approximated by smooth function in======�-integral form.

TL;DR: This paper transforms the constrained optimization problem into a penalized problem with only box constraints on the decision variables using a novel exact penalty function, and proves that this penalty function has continuous derivatives, and for a sufficiently large and finite penalty parameter, its local minimizer is feasible in the sense that the continuous inequality constraints are satisfied.

TL;DR: This paper introduces a novel penalty function to penalize constraint violations, and shows that this penalty function is exact-that is, when the penalty parameter is sufficiently large, any local Solution of the approximate problem can be used to generate a corresponding local solution of the original problem.

TL;DR: An optimal control problem in which the control takes values from a discrete set and the state and control are subject to continuous inequality constraints is considered, and a novel exact penalty function is introduced to penalize constraint violations.

TL;DR: The basic concepts underlying the MISER software, which include the control parameterization technique, a time scaling transform, a constraint transcription technique, and the co-state approach for gradient calculation, are described in this paper.