Pseudoconvex Proximal Splitting for L-Infty Problems in Multiview Geometry
Abstract
In this paper we study optimization methods for minimizing large-scale pseudoconvex L_infinity problems in multiview geometry. We present a novel algorithm for solving this class of problem based on proximal splitting methods. We provide a brief derivation of the proposed method along with a general convergence analysis. The resulting meta-algorithm requires very little effort in terms of implementation and instead makes use of existing advanced solvers for non-linear optimization. Preliminary experiments on a number of real image datasets indicate that the proposed method experimentally matches or outperforms current state-of-the-art solvers for this class of problems.
Cite
Text
Eriksson and Isaksson. "Pseudoconvex Proximal Splitting for L-Infty Problems in Multiview Geometry." Conference on Computer Vision and Pattern Recognition, 2014.Markdown
[Eriksson and Isaksson. "Pseudoconvex Proximal Splitting for L-Infty Problems in Multiview Geometry." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/eriksson2014cvpr-pseudoconvex/)BibTeX
@inproceedings{eriksson2014cvpr-pseudoconvex,
title = {{Pseudoconvex Proximal Splitting for L-Infty Problems in Multiview Geometry}},
author = {Eriksson, Anders and Isaksson, Mats},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2014},
url = {https://mlanthology.org/cvpr/2014/eriksson2014cvpr-pseudoconvex/}
}