State-of-The-Art Sparse Direct Solvers
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Abstract (arXiv)
In this chapter we will give an insight into modern sparse elimination methods. These are driven by a preprocessing phase based on combinatorial algorithms which improve diagonal dominance, reduce fill-in, and improve concurrency to allow for parallel treatment. Moreover, these methods detect dense submatrices which can be handled by dense matrix kernels based on multithreaded level-3 BLAS. We will demonstrate for problems arising from circuit simulation, how the improvements in recent years have advanced direct solution methods significantly.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2019.
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- 2025ReFill: Reinforcement Learning for Fill-In Minimizationcited by 0
- 2025ReFill: Reinforcement Learning for Fill-In Minimization27 Jan 2025