Berkeley Lab - Scientific Computing Seminar

Date:

Tuesday, February 5, 2008

Time:

11:00am-12:00pm

Location:

Building 50F, 1647 Conference Room

Seminar Speaker:

Erika Fuentes
Department of Computer Science
University of Tennessee, Knoxville

Title:

Statistical Learning and Data Mining Techniques for Algorithm Selection for Solving Sparse Linear Systems

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 based on their individual properties.

Sponsor of Seminar:

David Skinner

Scientific Computing

Contact Esmond G. Ng EGNg@lbl.gov