2 citations · 3 across the 4 of their papers we have counts for
10 papers
Parallelized integrated nested Laplace approximations for fast Bayesian inference
Lisa Gaedke-Merzhäuser, Janet van Niekerk, Olaf Schenk +1
There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work w…
Reduced-Space Interior Point Methods in Power Grid Problems
Juraj Kardos, Drosos Kourounis, Olaf Schenk
Due to critical environmental issues, the power systems have to accommodate a significant level of penetration of renewable generation which requires smart approaches to the power…
High Performance Block Incomplete LU Factorization
Matthias Bollhöfer, Olaf Schenk, Fabio Verbosio
Many application problems that lead to solving linear systems make use of preconditioned Krylov subspace solvers to compute their solution. Among the most popular preconditioning a…
New frontiers in Bayesian modeling using the INLA package in R
Janet van Niekerk, Haakon Bakka, Haavard Rue +1
The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It…
A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication
Christie L. Alappat, Georg Hager, Olaf Schenk +5
The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building block for many numerical linear algebra kernel operations or graph traversal applications. Par…
Structure Exploiting Interior Point Methods
Juraj Kardoš, Drosos Kourounis, Olaf Schenk
Interior point methods are among the most popular techniques for large scale nonlinear optimization, owing to their intrinsic ability of scaling to arbitrary large problem sizes. T…