In this section, we report our experiments on the accuracy and response time of the condition number prediction methods. We use SVMlight for SVM regression. There are 277 matrices from Matrix Market tested in the experiments. We use altogether 60 matrix features. First, We test how accurate the predicted condition numbers are, compared with the directly computed condition numbers. The figure below illustrates the percentage of all matrices for which the relative differences between the computed values and the predicted values of the condition number are within 10, 10^2, 10^3, 10^4, respectively.

6 seconds is also an acceptable time for an online query systems. Prediction with feature selection is only slightly faster than without any feature selection. Using half of the features does not mean reducing the time cost by half.

The prediction method is especially advantageous in response time for large size matrices. For example, in Table 2 the average LAPACK response time for the 78 matrices with size larger than 2000 is around 6 minutes, while using the prediction methods, the response time is only about 20 seconds.

Table 3 gives some examples of how the prediction methods exceed the LAPACK method in response time. For instance, LAPACK uses about two and a half hours to compute the condition number of the matrix ADD20, the prediction methods only need less than one second.