@inproceedings{a60e5c20595a40e6a93943fc9f74fbae,
title = "A hybrid steepest descent method for L-infinity geometry problems",
abstract = "Recent work on geometric vision problems has exploited convexity properties to obtain globally optimal solutions. The way based on L-infinity norm makes it possible to obtain a provably global optimal solution. But the computation time increases rapidly according to the size of measurement data, so the time cost is unbearable for large scale data. We validate that L-infinity geometry problems is a variational inequality problem essentially and present a hybrid steepest descent method instead of traditional interior point algorithm to compute L-infinity solutions for large scale geometry problem. We give both theoretic justification and experimental verification. Experimental results verify that our method is extremely fast than traditional ones while keeps the accuracy.",
keywords = "Global optimization, KKT, L-infinity norm, Triangulation, Variational Inequality",
author = "Guoqing Zhou and Qing Wang",
year = "2012",
doi = "10.1007/978-3-642-31919-8\_59",
language = "英语",
isbn = "9783642319181",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "458--465",
booktitle = "Intelligent Science and Intelligent Data Engineering - Second Sino-Foreign-Interchange Workshop, IScIDE 2011, Revised Selected Papers",
note = "2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011 ; Conference date: 23-10-2011 Through 25-10-2011",
}