optimization

Unit Commitment by Augmented Lagrangian Relaxation: Testing Two Decomposition Approaches

Publication TypeJournal Article
Year of Publication2002
AuthorsBeltran, C.; Heredia, F. J.
Journal TitleJournal of Optimization Theory and Applications
VolumeV112
Issue2
Pages295 - 314
Journal Date02/2002
PublisherSpringer Netherlands
Key Wordsaugmented lagrangian relaxation; generalized unit commitment; block coordinated descent method; auxiliary principle problem; research; paper
AbstractOne of the main drawbacks of the augmented Lagrangian relaxation method is that the quadratic term introduced by the augmented Lagrangian is not separable. We compare empirically and theoretically two methods designed to cope with the nonseparability of the Lagrangian function: the auxiliary problem principle method and the block coordinated descent method. Also, we use the so-called unit commitment problem to test both methods. The objective of the unit commitment problem is to optimize the electricity production and distribution, considering a short-term planning horizon.
URLClick Here
DOI10.1023/A:1013601906224
ExportTagged XML BibTex

An Effective Line Search for the Subgradient Method

Publication TypeJournal Article
Year of Publication2005
AuthorsBeltran C.; F.-Javier Heredia
Journal TitleJournal of Optimization Theory and Applications
Volume125
Issue1
Pages19
Start Page1
ISSN Number0022-3239
Key Wordslagrangian relaxation; generalized unit commitment; radar subgradient method; research; paper
AbstractOne of the main drawbacks of the subgradient method is the tuning process to determine the sequence of steplengths. In this paper, the radar subgradient method, a heuristic method designed to compute a tuning-free subgradient steplength, is geometrically motivated and algebraically deduced. The unit commitment problem, which arises in the electrical engineering field, is used to compare the performance of the subgradient method with the new radar subgradient method.
URLClick Here
DOI10.1007/s10957-004-1708-4
ExportTagged XML BibTex
Syndicate content