Direct methods for sparse matrices
Direct methods for sparse matrices
How fast are nonsymmetric matrix iterations
SIAM Journal on Matrix Analysis and Applications
Towards polyalgorithmic linear system solvers for nonlinear elliptic problems
SIAM Journal on Scientific Computing
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Faster PDE-based simulations using robust composite linear solvers
Future Generation Computer Systems - Special issue: Selected numerical algorithms
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Neural Networks for Predicting the Behavior of Preconditioned Iterative Solvers
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
On Using Reinforcement Learning to Solve Sparse Linear Systems
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
| Hi-index | 0.00 |
The time to solve linear systems depends to a large extent on the choice of the solution method and the properties of the coefficient matrix. Although there are several linear solution methods, in most cases it is impossible to predict apriori which linear solver would be best suited for a given linear system. Recent investigations on selecting linear solvers for a given system have explored the use of classification techniques based on the linear system parameters for solver selection. In this paper, we present a method to develop low-cost high-accuracy classifiers. We show that the cost for constructing a classifier can be significantly reduced by focusing on the computational complexity of each feature. In particular, we filter out low information linear system parameters and then order the remaining parameters to decrease the total computation cost for classification at a prescribed accuracy. Our results indicate that the speedup factor of the time to compute the feature set using our ordering can be as high as 262. The accuracy and computation time of the feature set generated using our method is comparable to a near-optimal one, thus demonstrating the effectiveness of our technique.