Abstract
In this paper, we consider a linearly constrained quadratic programming (QP) problem arising from cross directional control of large papermaking processes. Different from general-purpose QP solvers, we solve the optimization problem by taking advantage of the problem structure and features, such as positive-definiteness of the Hessian matrix, sparsity of the Hessian and constraint matrices. It is implemented based on a dual feasible, active-set algorithm, a Schur complement method and a warm start strategy. The Schur complement is proved to be nonsingular throughout iterations, which makes the solver numerically very reliable. In comparison with the standard Matlab QP solver, the proposed QP solver is much more efficient in the case studies we performed on real industrial papermaking processes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
