Solving elliptic problems using ELLPACK
Solving elliptic problems using ELLPACK
Programming pearls
ACM Transactions on Mathematical Software (TOMS)
Automated selection of mathematical software
ACM Transactions on Mathematical Software (TOMS)
ODEXPERT: an expert system to select numerical solvers for initial value ODE systems
ACM Transactions on Mathematical Software (TOMS)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Memory aspects and performance of iterative solvers
SIAM Journal on Scientific Computing
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
PYTHIA: a knowledge-based system to select scientific algorithms
ACM Transactions on Mathematical Software (TOMS)
A database perspective on knowledge discovery
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Iterative methods for solving linear systems
Iterative methods for solving linear systems
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
PELLPACK: a problem-solving environment for PDE-based applications on multicomputer platforms
ACM Transactions on Mathematical Software (TOMS)
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Mathematical Software (TOMS) - Special issue in honor of John Rice's 65th birthday
Algorithm 625: A Two-Dimensional Domain Processor
ACM Transactions on Mathematical Software (TOMS)
Note on generalization in experimental algorithmics
ACM Transactions on Mathematical Software (TOMS)
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Graphical encoding for information visualization: using icon color, shape, and size to convey nominal and quantitative data
Novel runtime systems support for adaptive compositional modeling in PSEs
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
On Using Reinforcement Learning to Solve Sparse Linear Systems
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
A Standard and Software for Numerical Metadata
ACM Transactions on Mathematical Software (TOMS)
Novel runtime systems support for adaptive compositional modeling in PSEs
Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
Using hierarchical data mining to characterize performance of wireless system configurations
Advances in Engineering Software
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In this paper we extend previous work in mining recommendation spaces based on symbolic problem features to PDE problems with continuous-valued attributes. We identify the research issues in mining such spaces, present a dynamic programming algorithm form the data-mining literature, and describe how a priori domain metaknowledge can be used to control the complexity of induction. A visualization aid for continuous-valued recommendation spaces is also outlined. Two case studies are presented to illustrate our approach and tools: (i) a comparison of an iterative and a direct linear system solver on nearly singular problems, and (ii) a comparison of two iterative solvers on problems posed on nonrectangular domains. Both case studies involve continuously varying problem and method parameters which strongly influence the choice of best algorithm in particular cases. By mining the results from thousands of PDE solves, we can gain valuable insight into the relative performance of these methods on similar problems.