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

Author

Erika Fuentes, University of Tennessee - Knoxville

Date of Award

12-2007

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Computer Science

Major Professor

Jack Dongarra

Committee Members

J.D. Birdwell, Victor Eijkhout, Lynne Parker, Tsewei Wang

Abstract

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.

Recommended Citation

Fuentes, Erika, "Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems. " PhD diss., University of Tennessee, 2007.
https://trace.tennessee.edu/utk_graddiss/171