An overview of the recent results on economic model predictive control (EMPC) is presented
and discussed addressing both closed-loop stability and performance for nonlinear systems …

Optimizing process economics in model predictive control traditionally has been done using
a two-step approach in which the economic objectives are first converted to steady-state …

Combinatorial techniques have become essential tools across the landscape of
computational science, and some of the combinatorial ideas undergirding these tools are …

The computation of a sparse Hessian matrix H using automatic differentiation (AD) can be
made efficient using the following four-step procedure:(1) Determine the sparsity structure of …

Our goal is to improve variance reducing stochastic methods through better control variates.
We first propose a modification of SVRG which uses the Hessian to track gradients over …

We investigate the computation of Hessian matrices via Automatic Differentiation, using a
graph model and an algebraic model. The graph model reveals the inherent symmetries …

Algorithmic differentiation (AD) is a mathematical concept which evolved over the last
decades to a very robust and well understood tool for computation of derivatives. It can be …

Using a model from a Chromatographic separation process in chemical engineering, we
demonstrate that large, sparse Jacobians of fairly complex structures can be computed …

Efficient calculation of the solutions of nonlinear optimal control problems (NOCPs) is
becoming more and more important for today's control engineers. The systems to be …

A fundamental need that occurs across the field of scientific computing is the calculation of
partial derivatives. For example, to determine the direction of sharpest ascent of a …