Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for locomotion
and manipulation which leverages trajectory optimization at each control step. While the …

Current Graphics Processing Units (GPUs) are capable of obtaining high computational
performance in scientific applications. Nevertheless, programmers have to use suitable …

Partial pivoting is the method of choice to ensure stability in matrix factorizations performed
on CPUs. For sparse matrices, this has not been implemented on GPUs so far because of …

The tridiagonal solver is an important kernel and is widely supported in mainstream
numerical libraries. While parallel algorithms have been studied for many-core architectures …

Featured Application The guidance of uncrewed aerial/ground vehicles, high-end security,
surveillance, and various 3D manipulation, inspection, and measurements. Abstract …

The paper presents some results of research of applicability of various computing schemes
of implementing QR and LQ matrix decompositions for shared memory parallel systems …

Whole-body, nonlinear model predictive control (MPC) refers to the control strategy where a
robot's state and input trajectories are continually optimized over a finite time horizon while …

We consider the computation of a few eigenpairs of a generalized eigenvalue problem A x=
λ B x with block-tridiagonal matrices, not necessarily symmetric, in the context of Krylov …

We propose a parallel sparse triangular linear system solver based on the Spike algorithm.
Sparse triangular systems are required to be solved in many applications. Often, they are a …

Gauss--Seidel (GS) is a widely used iterative method for solving sparse linear systems of
equations and also known to be effective as a smoother in algebraic multigrid methods …