@Article{JCM-19-1,
author = {Yuan, Ya-Xiang},
title = {A Scaled Central Path for Linear Programming},
journal = {Journal of Computational Mathematics},
year = {2001},
volume = {19},
number = {1},
pages = {35--40},
abstract = {<p style="text-align: justify;">Interior point methods are very efficient methods for solving large scale linear programming problems. The central path plays a very important role in interior point methods. In this paper we propose a new central path, which scales the variables. Thus it has the advantage of forcing the path to have roughly the same distance from each active constraint boundary near the solution.</p>},
issn = {1991-7139},
doi = {https://doi.org/2001-JCM-8955},
url = {https://global-sci.com/article/85445/a-scaled-central-path-for-linear-programming}
}