Multi-stage Learning of Linear Algebra Algorithms

In evolving applications, there is a need for the dynamic selection of algorithms or algorithm parameters. Such selection is hardly ever governed by exact theory, so intelligent recommender systems have been proposed. In our application area, the iterative solution of linear systems of equations, the recommendation process is especially complicated, since the classes have a multi-dimensional structure. We discuss different strategies of recommending the different components of the algorithms.

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Victor Eijkhout, Erika Fuentes
• Keywords: Algorithm Parameters, ICMLA 2008, Intelligent Recommender Systems, Machine Learning, Such Selection