Session 1

Weifeng Liu : Pangulu: A Scalable Regular Two-Dimensional Block-Cyclic Sparse Direct Solver on Distributed Heterogeneous Systems

Samuel Rodriguez: Accelerating Sparse Direct Solvers: Strategies for High Performance on NVIDIA GPUs

Théo Briquet: Learning for Predicting the Rank of Hierarchical Matrices

Sherry Li: Construction of Hierarchically Semi-Separable Matrix Using Faster Randomized Sketching

Nabil Abubaker: Scaling Stratified Stochastic Gradient Descent for Distributed Matrix Completion

Session 2

Barbara Wohlmuth: Large Scale Generation of Matern Type Random Fields

Jérémy Briant: A Filtered Multilevel Monte Carlo Method for the Estimation of Discretized Random Fields

Jean-Guillaume De Damas: Randomized Implicilty Restarted Arnoldi Method for Large and Sparse Non-Symmetric Eigenproblems

Session 3

Petr Vacek: The Effect of Approximate Coarsest-Level Solves on the Convergence of Multigrid V-Cycle Methods

Michael Zikeli: Mixed Precision Multigrid Methods on Hybrid Tetrahedral Grids

Dinesh Parthasarathy: Evolving Algebraic Multigrid Methods using Grammar-Guided Genetic Programming

Hadrien Godé: Comparison of Multigrid and Machine Learning-Based Poisson Solvers

Session 4

Frédéric Nataf: Adaptive Coarse Space for Saddle Point Problem

El-Mehdi Ettaouchi: Nonlinear Schwarz Domain Decomposition Solvers for Two-Phase Flow in Porous Media

Miroslav Tůma: A Half Precision Incomplete Cholesky Factorization Preconditioning

Ieva Daužickaitė: Mixed Precision FGMRES

Murat Manguoglu: A Null-Space Method Based Algebraic Iterative Scheme for Saddle Point Problems