X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/16b4107f3a70f199b9c04d0346050f4f52b5114b..17bff40b83bcdcc39769f9e59c70ffae1c525b72:/BookGPU/Chapters/chapter11/ch11.tex

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--- a/BookGPU/Chapters/chapter11/ch11.tex
+++ b/BookGPU/Chapters/chapter11/ch11.tex
@@ -346,7 +346,7 @@ A popular PAV algorithm (PAVA) is one method that provides efficient numerical s
 
 Various serial implementations of the PAVA exist. It is noted \cite{Kearsley_2006} that in PAVA, which is based on the ideas from convex analysis, a decomposition theorem holds, namely, performing PAVA separately on two contiguous subsets of data and then performing PAVA on the result produces isotonic regression on the whole data set. Thus, isotonic regression is parallelizable, and the divide-and-conquer approach, decomposing the original problem into two smaller subproblems, can be implemented on multiple processors. However, to our knowledge, no parallel PAVA for many-core systems such as GPUs exist.
 
-\index{MLS algorithm} \index{Minimum Lower Sets}
+\index{MLS (minimum lower sets) algorithm} 
 Another approach to isotonic regression is called the MLS algorithm 
 \cite{Best1990, Robertson_book}. It provides the same solution as the PAVA, but works differently.  For each datum (or block), MLS selects the largest contiguous block of subsequent data with the smallest average. If this average is smaller than that of the preceding block, the blocks are merged, and the data in the block are replaced with their average. MLS is also an active set method \cite{Best1990}, but its complexity is $O(n^2)$ as opposed to $O(n)$ of the PAVA, and of another active set algorithm proposed in \cite{Best1990} by the name of Algorithm A.