Solving elliptic problems using ELLPACK
Solving elliptic problems using ELLPACK
ACM Transactions on Mathematical Software (TOMS)
Automated selection of mathematical software
ACM Transactions on Mathematical Software (TOMS)
PYTHIA: a knowledge-based system to select scientific algorithms
ACM Transactions on Mathematical Software (TOMS)
PELLPACK: a problem-solving environment for PDE-based applications on multicomputer platforms
ACM Transactions on Mathematical Software (TOMS)
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
Concise, intelligible, and approximate profiling of multiple classes
International Journal of Human-Computer Studies - Special issue on Machine Discovery
On Reporting Computational Experiments with Mathematical Software
ACM Transactions on Mathematical Software (TOMS)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
The Development and Implementation of a Performance Database Server
The Development and Implementation of a Performance Database Server
Recommender systems of problem-solving environments
Recommender systems of problem-solving environments
Mining and visualizing recommendation spaces for elliptic PDEs with continuous attributes
ACM Transactions on Mathematical Software (TOMS) - Special issue in honor of John Rice's 65th birthday
Handbook of data mining and knowledge discovery
Mining and visualizing recommendation spaces for PDE solvers: the continuous attributes case
Computational science, mathematics and software
Statistical Models for Empirical Search-Based Performance Tuning
International Journal of High Performance Computing Applications
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A recurring theme in mathematical software evaluation is the generalization of rankings of algorithms on test problems to build knowledge-based recommender systems for algorithm selection. A key issue is to profile algorithms in terms of the qualitative characteristics of benchmark problems. In this methodological note, we adapt a novel all-pairs algorithm for the profiling task; given performance rankings for m algorithms on n problem instances, each described with p features, identify a (minimal) subset of p that is useful for assessing the selective superiority of an algorithm over another, for all pairs of m algorithms. We show how techniques presented in the mathematical software literature are inadequate for such profiling purposes. In conclusion, we also address various statistical issues underlying the effective application of this technique.