Performance-Based Numerical Solver Selection in the Lighthouse Framework

DOI10.1137/15M1028406zbMath1352.65107MaRDI QIDQ2830643

Kanika Sood, E. R. Jessup, Pate Motter, Boyana Norris

Publication date: 28 October 2016

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

zbMATH Keywords

iterative algorithmmachine learningexpert systemsparse linear systemtaxonomyLighthouse projectsoftware collections

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Learning and adaptive systems in artificial intelligence (68T05) Other programming paradigms (object-oriented, sequential, concurrent, automatic, etc.) (68N19) Iterative numerical methods for linear systems (65F10) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35) Numerical algorithms for specific classes of architectures (65Y10)

Related Items (2)

Automated tuning for the parameters of linear solvers ⋮ Tuning soft mutations of the evolution algorithm for optimizing the linear solver parameters

Uses Software

• ATLAS
• LAPACK
• PYTHIA
• SLEPc
• BLAS
• PETSc
• MatrixMarket
• Trilinos
• SparseMatrix
• WEKA
• Scikit
• C4.5
• FUN3D
• Decision tree for optimization software
• GCO
• Lighthouse
• Django

Cites Work

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• Bagging predictors
• Data mining. Concepts and techniques
• Support-vector networks
• Algorithmic bombardment for the iterative solution of linear systems: A poly-iterative approach
• The university of Florida sparse matrix collection
• The $25,000,000,000 Eigenvector: The Linear Algebra behind Google
• SLEPc
• Performance and Accuracy of LAPACK's Symmetric Tridiagonal Eigensolvers
• LAPACK Users' Guide
• Towards Polyalgorithmic Linear System Solvers for Nonlinear Elliptic Problems
• PYTHIA
• Computing the Bidiagonal SVD Using Multiple Relatively Robust Representations
• Data Mining and Knowledge Discovery Handbook
• Automated empirical optimizations of software and the ATLAS project
• The elements of statistical learning. Data mining, inference, and prediction

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