scholarly journals Rounding Algorithm of Minimizing Executing and Transmitting Time in Multiple CRANs

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chia-Cheng Hu ◽  
Wen-Wu Liu ◽  
Jeng-Shyang Pan
Keyword(s):  
Rounding Algorithm

scholarly journals Evaluation of lossless and lossy algorithms for the compression of scientific datasets in NetCDF-4 or HDF5 formatted files

2018 ◽  
Author(s):  
Xavier Delaunay ◽  
Aurélie Courtois ◽  
Flavien Gouillon
Keyword(s):  
Data Storage ◽  
Compression Ratio ◽  
Reduction Method ◽  
High Compression Ratio ◽  
Compression Speed ◽  
File Formats ◽  
Scientific Datasets ◽  
Bounded Data ◽  
Data Reduction Method ◽  
Rounding Algorithm

Abstract. The increasing volume of scientific datasets imposes the use of compression to reduce the data storage or transmission costs, specifically for the oceanography or meteorological datasets generated by Earth observation mission ground segments. These data are mostly produced in NetCDF formatted files. Indeed, the NetCDF-4/HDF5 file formats are widely spread in the global scientific community because of the nice features they offer. Particularly, the HDF5 offers the dynamically loaded filter plugin functionality allowing users to write filters, such as compression/decompression filters, to process the data before reading or writing it on the disk. In this work, we evaluate the performance of lossy and lossless compression/decompression methods through NetCDF-4 and HDF5 tools on analytical and real scientific floating-point datasets. We also introduce the Digit Rounding algorithm, a new relative error bounded data reduction method inspired by the Bit Grooming algorithm. The Digit Rounding algorithm allows high compression ratio while preserving a given number of significant digits in the dataset. It achieves higher compression ratio than the Bit Grooming algorithm while keeping similar compression speed.

Decimal CORDIC Rotation based on Selection by Rounding: Algorithm and Architecture

2011 ◽  
Vol 54 (11) ◽  
pp. 1798-1809 ◽  
Author(s):  
A. Kaivani ◽  
G. Jaberipur
Keyword(s):  
Rounding Algorithm

scholarly journals ON THE PIPAGE ROUNDING ALGORITHM FOR SUBMODULAR FUNCTION MAXIMIZATION — A VIEW FROM DISCRETE CONVEX ANALYSIS

2009 ◽  
Vol 01 (01) ◽  
pp. 1-23 ◽  
Author(s):  
AKIYOSHI SHIOURA
Keyword(s):  
Approximation Algorithm ◽  
Convex Analysis ◽  
Submodular Function ◽  
Submodular Functions ◽  
Concave Functions ◽  
Discrete Convex Analysis ◽  
Discrete Convex ◽  
Weighted Rank ◽  
Rounding Algorithm ◽  
Rank Functions

We consider the problem of maximizing a nondecreasing submodular set function under a matroid constraint. Recently, Calinescu et al. (2007) proposed an elegant framework for the approximation of this problem, which is based on the pipage rounding technique by Ageev and Sviridenko (2004), and showed that this framework indeed yields a (1 - 1/e)-approximation algorithm for the class of submodular functions which are represented as the sum of weighted rank functions of matroids. This paper sheds a new light on this result from the viewpoint of discrete convex analysis by extending it to the class of submodular functions which are the sum of M ♮-concave functions. M ♮-concave functions are a class of discrete concave functions introduced by Murota and Shioura (1999), and contain the class of the sum of weighted rank functions as a proper subclass. Our result provides a better understanding for why the pipage rounding algorithm works for the sum of weighted rank functions. Based on the new observation, we further extend the approximation algorithm to the maximization of a nondecreasing submodular function over an integral polymatroid. This extension has an application in multi-unit combinatorial auctions.

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