Matrix condition number prediction with SVM regression and feature selection

Condition number of a matrix is an important measure in numerical analysis and linear algebra. The general approach to obtaining it is through direct computation or estimation The time and memory cost of such approaches are very high, especially for large size matrices. We propose a totally differ ent approach to estimating the condition number of a sparse matrix. That is, after computing the features of a matrix, we use support vector regression (SVR) to predict its condition number. We also use feature selection strategies to further reduce the response time and improve accuracy. We de sign a feature selection criterion which combines the weights from SVR with the weights from comparison of matrices with their preconditioned counterparts. Our preliminary experi ments show that the results are encouraging.