33 citations · 63 across the 10 of their papers we have counts for
11 papers
ExaModels.jl: an Algebraic Modeling System for Nonlinear Programming on GPUs
Sungho Shin, Michel Schanen, François Pacaud +2
Large-scale nonlinear programs almost always exhibit partially separable and repetitive structure, yet most existing algebraic modeling systems do not take advantage of it. A nonli…
A General and Streamlined Differentiable Optimization Framework
Andrew W. Rosemberg, Joaquim Dias Garcia, François Pacaud +5
Differentiating through constrained optimization problems is increasingly central to learning, control, and large-scale decision-making systems, yet practical integration remains c…
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…
GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP
François Pacaud, Sungho Shin
We investigate the potential of Graphics Processing Units (GPUs) to solve large-scale nonlinear programs with a dynamic structure. Using ExaModels, a GPU-accelerated automatic diff…
Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods
Sungho Shin, François Pacaud, Mihai Anitescu
This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkabl…
Parallel Interior-Point Solver for Block-Structured Nonlinear Programs on SIMD/GPU Architectures
François Pacaud, Michel Schanen, Sungho Shin +2
We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decomp…
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
Alexander Engelmann, Sungho Shin, François Pacaud +1
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of i…
Constrained Policy Optimization for Stochastic Optimal Control under Nonstationary Uncertainties
Sungho Shin, François Pacaud, Emil Contantinescu +1
This article presents a constrained policy optimization approach for the optimal control of systems under nonstationary uncertainties. We introduce an assumption that we call Marko…
Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
David Cole, Sungho Shin, François Pacaud +2
We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method red…
A Feasible Reduced Space Method for Real-Time Optimal Power Flow
François Pacaud, Daniel Adrian Maldonado, Sungho Shin +2
We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with sec…
Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition
Youngdae Kim, François Pacaud, Kibaek Kim +1
We present the implementation of a trust-region Newton algorithm ExaTron for bound-constrained nonlinear programming problems, fully running on multiple GPUs. Without data transfer…