132 citations · 249 across the 19 of their papers we have counts for
19 papers
Scalable Approximate Selected Inversion Based on Single-Level Incomplete Factorization and Spectral Corrections for Large Sparse Systems
Tahamina Akter, Matthias Bollhöfer, Olaf Schenk
This article introduces four parallel numerical techniques for computing entries of the inverse of large sparse symmetric systems, all grounded in incomplete (ILDL) factori…
AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study
Naga Venkata Sai Jitin Jami, Juraj Kardoš, Olaf Schenk +1
Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where el…
Sensitivity Analysis of High-Dimensional Models with Correlated Inputs
Juraj Kardos, Wouter Edeling, Diana Suleimenova +2
Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or stud…
Integrated Nested Laplace Approximations for Large-Scale Spatial-Temporal Bayesian Modeling
Lisa Gaedke-Merzhäuser, Elias Krainski, Radim Janalik +2
Bayesian inference tasks continue to pose a computational challenge. This especially holds for spatial-temporal modeling where high-dimensional latent parameter spaces are ubiquito…
Multiresolution kernel matrix algebra
H. Harbrecht, M. Multerer, O. Schenk +1
We propose a sparse algebra for samplet compressed kernel matrices, to enable efficient scattered data analysis. We show the compression of kernel matrices by means of samplets pro…
Level-based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector Multiplication
Christie L. Alappat, Georg Hager, Olaf Schenk +1
The multiplication of a sparse matrix with a dense vector (SpMV) is a key component in many numerical schemes and its performance is known to be severely limited by main memory acc…
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…
Multiway -spectral graph cuts on Grassmann manifolds
Dimosthenis Pasadakis, Christie Louis Alappat, Olaf Schenk +1
Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background.…
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…
State-of-The-Art Sparse Direct Solvers
Matthias Bollhöfer, Olaf Schenk, Radim Janalík +2
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 d…
Complete results for a numerical evaluation of interior point solvers for large-scale optimal power flow problems
Juraj Kardos, Drosos Kourounis, Olaf Schenk +1
Recent advances in open source interior-point optimization methods and power system related software have provided researchers and educators with the necessary platform for simulat…
Optimizing gas networks using adjoint gradients
Conor O'Malley, Drosos Kourounis, Gabriela Hug +1
An increasing amount of gas-fired power plants are currently being installed in modern power grids worldwide. This is due to their low cost and the inherent flexibility offered to…
Selected inversion as key to a stable Langevin evolution across the QCD phase boundary
Jacques Bloch, Olaf Schenk
We present new results of full QCD at nonzero chemical potential. In PRD 92, 094516 (2015) the complex Langevin method was shown to break down when the inverse coupling decreases a…
Compile-Time Symbolic Differentiation Using C++ Expression Templates
Drosos Kourounis, Leonidas Gergidis, Michael Saunders +2
Template metaprogramming is a popular technique for implementing compile time mechanisms for numerical computing. We demonstrate how expression templates can be used for compile ti…
On Large Scale Diagonalization Techniques for the Anderson Model of Localization
Olaf Schenk, Matthias Bollhoefer, Rudolf A. Roemer
We propose efficient preconditioning algorithms for an eigenvalue problem arising in quantum physics, namely the computation of a few interior eigenvalues and their associated eige…