10 papers · 1 filter
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…
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-…
Overlapping Schwarz Scheme for Linear-Quadratic Programs in Continuous Time
Hongli Zhao, Mihai Anitescu, Sen Na
We present an optimize-then-discretize framework for solving linear-quadratic optimal control problems (OCP) governed by time-inhomogeneous ordinary differential equations (ODEs).…
Condensed Interior-Point Methods for Scalable Nonlinear Programming on GPUs
François Pacaud, Sungho Shin, Alexis Montoison +2
This paper explores two variants of condensed-space interior-point methods designed for GPUs - HyKKT and LiftedKKT - by analyzing their numerical properties through error analysis…