This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …

Classical trust region methods were designed to solve problems in which function and
gradient information are exact. This paper considers the case when there are errors (or …

A worst-case complexity bound is proved for a sequential quadratic optimization (commonly
known as SQP) algorithm that has been designed for solving optimization problems …

A stochastic algorithm is proposed, analyzed, and tested experimentally for solving
continuous optimization problems with nonlinear equality constraints. It is assumed that …

We propose a trust-region stochastic sequential quadratic programming algorithm (TR-
StoSQP) to solve nonlinear optimization problems with stochastic objectives and …

We study nonlinear optimization problems with a stochastic objective and deterministic
equality and inequality constraints, which emerge in numerous applications including …

In this paper, we propose a stochastic method for solving equality constrained optimization
problems that utilizes predictive variance reduction. Specifically, we develop a method …

A sequential quadratic optimization algorithm for minimizing an objective function defined by
an expectation subject to nonlinear inequality and equality constraints is proposed …

A step-search sequential quadratic programming method is proposed for solving nonlinear
equality-constrained stochastic optimization problems. It is assumed that constraint function …

An algorithm is proposed, analyzed, and tested experimentally for solving stochastic
optimization problems in which the decision variables are constrained to satisfy equations …