Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems

Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems

There are many applications and problems in science and engineering that require large-scale numerical simulations and computations. The issue of choosing an appropriate method to solve these problems is very common, however it is not a trivial one, principally because this decision is most of the t...

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Bibliographic Details
Main Author: Fuentes, Erika
Published: Trace: Tennessee Research and Creative Exchange 2007
Subjects:
Computer Sciences
Theory and Algorithms
Online Access:http://trace.tennessee.edu/utk_graddiss/171
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spelling ndltd-UTENN-oai-trace.tennessee.edu-utk_graddiss-12182011-12-13T16:01:45Z Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems Fuentes, Erika There are many applications and problems in science and engineering that require large-scale numerical simulations and computations. The issue of choosing an appropriate method to solve these problems is very common, however it is not a trivial one, principally because this decision is most of the times too hard for humans to make, or certain degree of expertise and knowledge in the particular discipline, or in mathematics, are required. Thus, the development of a methodology that can facilitate or automate this process and helps to understand the problem, would be of great interest and help. The proposal is to utilize various statistically based machine-learning and data mining techniques to analyze and automate the process of choosing an appropriate numerical algorithm for solving a specific set of problems (sparse linear systems) based on their individual properties. 2007-12-01 text http://trace.tennessee.edu/utk_graddiss/171 Doctoral Dissertations Trace: Tennessee Research and Creative Exchange Computer Sciences Theory and Algorithms
collection NDLTD
sources NDLTD
topic Computer Sciences
Theory and Algorithms
spellingShingle Computer Sciences
Theory and Algorithms
Fuentes, Erika
Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
description There are many applications and problems in science and engineering that require large-scale numerical simulations and computations. The issue of choosing an appropriate method to solve these problems is very common, however it is not a trivial one, principally because this decision is most of the times too hard for humans to make, or certain degree of expertise and knowledge in the particular discipline, or in mathematics, are required. Thus, the development of a methodology that can facilitate or automate this process and helps to understand the problem, would be of great interest and help. The proposal is to utilize various statistically based machine-learning and data mining techniques to analyze and automate the process of choosing an appropriate numerical algorithm for solving a specific set of problems (sparse linear systems) based on their individual properties.
author Fuentes, Erika
author_facet Fuentes, Erika
author_sort Fuentes, Erika
title Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
title_short Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
title_full Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
title_fullStr Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
title_full_unstemmed Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
title_sort statistical and machine learning techniques applied to algorithm selection for solving sparse linear systems
publisher Trace: Tennessee Research and Creative Exchange
publishDate 2007
url http://trace.tennessee.edu/utk_graddiss/171
work_keys_str_mv AT fuenteserika statisticalandmachinelearningtechniquesappliedtoalgorithmselectionforsolvingsparselinearsystems
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