7 papers
Harnessing GPU Acceleration in Large-Scale Process Optimization
Boxun Huang, David Y. Shu, Michel Schanen +3
This paper presents a proof-of-concept workflow for equation-oriented process optimization that runs entirely on a GPU. Process optimization models often incorporate complex interc…
Parallel Sequential Quadratic Programming with Overlapping Graph Decomposition and Exact Augmented Lagrangian
Runxin Ni, Haoxuan Wang, Sen Na +2
In this paper, we address the challenge of solving large-scale graph-structured nonlinear programs (gsNLPs) in a scalable manner. GsNLPs are problems in which the objective and con…
Improved Approximation Bounds for Moore-Penrose Inverses of Banded Matrices with Applications to Continuous-Time Linear Quadratic Control
Sungho Shin, Wallace Gian Yion Tan, Mihai Anitescu
We present improved approximation bounds for the Moore-Penrose inverses of banded matrices, where the bandedness is induced by a metric on the index set. We show that the pseudoinv…
Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems
David Jin, Alexis Montoison, Sungho Shin
Block-tridiagonal systems are prevalent in state estimation and optimal control, and solving these systems is often the computational bottleneck. Improving the underlying solvers t…
GPU Implementation of Second-Order Linear and Nonlinear Programming Solvers
Alexis Montoison, François Pacaud, Sungho Shin +1
In recent years, GPU-accelerated optimization solvers based on second-order methods (e.g., interior-point methods) have gained momentum with the advent of mature and efficient GPU-…
MadNCL: A GPU Implementation of Algorithm NCL for Large-Scale, Degenerate Nonlinear Programs
Alexis Montoison, François Pacaud, Michael Saunders +2
We present a GPU implementation of Algorithm NCL, an augmented Lagrangian method for solving large-scale and degenerate nonlinear programs. Although interior-point methods and sequ…