1 citations · 1 across the 2 of their papers we have counts for
4 papers
Warm-starting active-set solvers using graph neural networks
Ella J. Schmidtobreick, Daniel Arnström, Paul Häusner +1
Quadratic programming (QP) solvers are widely used in real-time control and optimization, but their computational cost often limits applicability in time-critical settings. To reso…
Learning to accelerate distributed ADMM using graph neural networks
Henri Doerks, Paul Häusner, Daniel Hernández Escobar +1
Distributed optimization is fundamental to large-scale machine learning and control applications. Among existing methods, the alternating direction method of multipliers (ADMM) has…
Learning incomplete factorization preconditioners for GMRES
Paul Häusner, Aleix Nieto Juscafresa, Jens Sjölund
Incomplete LU factorizations of sparse matrices are widely used as preconditioners in Krylov subspace methods to speed up solving linear systems. Unfortunately, computing the preco…
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
Paul Häusner, Ozan Ãktem, Jens Sjölund
The convergence of the conjugate gradient method for solving large-scale and sparse linear equation systems depends on the spectral properties of the system matrix, which can be im…