Alternating Direction Method of Multipliers (ADMM) Techniques for Embedded Mixed-Integer Quadratic Programming and Applications
| dc.contributor.author | Liu, Jiaqi | |
| dc.contributor.supervisor | Lu, Tao | |
| dc.date.accessioned | 2020-05-13T23:53:23Z | |
| dc.date.available | 2020-05-13T23:53:23Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-05-13 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Engineering M.Eng. | en_US |
| dc.description.abstract | In this project, we delve into an important class of constrained nonconvex problems known as mixed-integer quadratic programming (MIQP). The popularity of MIQP is primarily due to the fact that many real-world problems can be described via MIQP models. The development of efficient MIQP algorithms has been an active and rapidly evolving field of research. As a matter of fact, previously well-known techniques for MIQP problems are found unsuitable for large-scale or online MIQP problems where algorithm’s computational efficiency is a crucial factor. In this regard, the alternating direction method of multipliers (ADMM) as a heuristic has shown to offer satisfactory suboptimal solutions with much improved computational complexity relative to global solvers based on for example branch-and-bound. This project provides the necessary details required to understand the ADMM-based algorithms as applied to MIQP problems. Three illustrative examples are also included in this project to demonstrate the effectiveness of the ADMM algorithm through numerical simulations and performance comparisons. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11749 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.title | Alternating Direction Method of Multipliers (ADMM) Techniques for Embedded Mixed-Integer Quadratic Programming and Applications | en_US |
| dc.type | project | en_US |