TY - JOUR
T1 - Review of Methods Inspired by Algebraic-Multigrid for Data and Image Analysis Applications
AU - Meirav Galun, Ronen Basri & Irad Yavneh
JO - Numerical Mathematics: Theory, Methods and Applications
VL - 2
SP - 283
EP - 312
PY - 2015
DA - 2015/08
SN - 8
DO - http://doi.org/10.4208/nmtma.2015.w14si
UR - https://global-sci.org/intro/article_detail/nmtma/12411.html
KW - 
AB - <p style="text-align: justify;">Algebraic Multigrid (AMG) methods were developed originally for numerically solving Partial Differential Equations (PDE), not necessarily on structured grids. In the last two decades solvers inspired by the AMG approach, were developed for non PDE problems, including data and image analysis problems, such as clustering, segmentation, quantization and others. These solvers share a common principle that there is a crosstalk between fine and coarse representations of the problems, with flow of information in both directions, fine-to-coarse and coarse-to-fine. This paper surveys some of these problems and the AMG-inspired algorithms for their solution.</p>