English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In optimization routines used for on-line Model Predictive Control (MPC),linear systems of equations are usually solved in each iteration. This is trueboth for Active Set (AS) methods as well as for Interior Point (IP) methods,and for linear MPC as well as for nonlinear MPC and hybrid MPC. The maincomputational effort is spent while solving these linear systems of equations,and hence, it is of greatest interest to solve them efficiently. Classically,the optimization problem has been formulated in either of two different ways.One of them leading to a sparse linear system of equations involving relativelymany variables to solve in each iteration and the other one leading to a denselinear system of equations involving relatively few variables. In this work, itis shown that it is possible not only to consider these two distinct choices offormulations. Instead it is shown that it is possible to create an entirefamily of formulations with different levels of sparsity and number ofvariables, and that this extra degree of freedom can be exploited to get evenbetter performance with the software and hardware at hand. This result alsoprovides a better answer to an often discussed question in MPC; should thesparse or dense formulation be used. In this work, it is shown that the answerto this question is that often none of these classical choices is the bestchoice, and that a better choice with a different level of sparsity actuallycan be found.

Controlling the level of sparsity in MPC