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Name: SPEX: SParse EXact linear algebra and optimization solvers
Description: The primary goal of this project is to design, create, and deploy computational tools to solve large-scale, sparse systems of linear equations and optimization problems without any error at all. Because of the ubiquity of solving systems of linear equations and optimization problems, the outcomes of this project will directly translate in software that is more reliable for applications across academia, industry, and government.
Type: Code Optimization and Modernization Tool
Keywords: Optimization problem, Algorithms as Software - solve large-scale, sparse LPs
License: LGPL-3.0-or-later
Project URL: https://github.com/clouren/SPEX
Download URL: https://github.com/clouren/SPEX/releases/tag/v1.1.2
Installation URL: https://github.com/clouren/SPEX/blob/Release/README.md
Software Stack: C, Fortran, TeX, MATLAB, HTML
Other products that use this product:
Citation: Please see acknowledgement section
Acknowledgement: Please cite the following papers: Lourenco C., E. Moreno-Centeno, “Algorithm 1XXX: SPEX Left LU, Exactly Solving Sparse Linear Systems via a Sparse Left-Looking Integer-Preserving LU Factorization”, ACM Transactions on Mathematical Software, Accepted Feb 2022. Lourenco C., E. Moreno-Centeno, “Exactly Solving Sparse Rational Linear Systems via Roundoff- Error-Free Cholesky Factorizations”, SIAM Journal on Matrix Analysis and Applications, Accepted Oct 2021. Lourenco C., A. R. Escobedo, E. Moreno-Centeno, T. A. Davis, “Exact Solution of Sparse Linear Systems via Left-Looking Roundoff-Error-Free LU Factorization in Time Proportional to Arithmetic Work”, SIAM Journal on Matrix Analysis and Applications, 40(2), 2019, 609–638.
Funding Agency: NSF
Award No: 1835499
Funded Project Title: Elements: Software: Roundoff-Error-Free Algorithms for Large-Scale, Sparse Systems of Linear Equations and Optimization
PI Name: Erick Moreno-Centeno
PI Email: emc@tamu.edu
Related Products: SuiteSparse
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