Zobrazeno 1 - 10
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pro vyhledávání: '"Pacaud, François"'
This paper explores two condensed-space interior-point methods to efficiently solve large-scale nonlinear programs on graphics processing units (GPUs). The interior-point method solves a sequence of symmetric indefinite linear systems, or Karush-Kuhn
Externí odkaz:
http://arxiv.org/abs/2405.14236
Autor:
Pacaud, François, Shin, Sungho
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 differentiation tool, and the interior-point solver MadNLP, we significant
Externí odkaz:
http://arxiv.org/abs/2403.15913
This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkable performance in various computing domains, their application in ACOPF
Externí odkaz:
http://arxiv.org/abs/2307.16830
We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-point algorithm into two successive operations: the e
Externí odkaz:
http://arxiv.org/abs/2301.04869
The real-time operation of large-scale infrastructure networks requires scalable optimization capabilities. Decomposition schemes can help achieve scalability; classical decomposition approaches such as the alternating direction method of multipliers
Externí odkaz:
http://arxiv.org/abs/2212.11571
This article presents a constrained policy optimization approach for the optimal control of systems under nonstationary uncertainties. We introduce an assumption that we call Markov embeddability that allows us to cast the stochastic optimal control
Externí odkaz:
http://arxiv.org/abs/2209.13050
We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method reduces the MPC problem by eliminating the state variables and applies a
Externí odkaz:
http://arxiv.org/abs/2209.13049
In this study, a microgrid with storage (battery, hot water tank) and solar panel is considered. We benchmark two algorithms, MPC and SDDP, that yield online policies to manage the microgrid, and compare them with a rule based policy. Model Predictiv
Externí odkaz:
http://arxiv.org/abs/2205.07700
The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the Karush--Kuhn--Tucker (KKT) system at each iteration of the algorithm.
Externí odkaz:
http://arxiv.org/abs/2203.11875
Publikováno v:
In Electric Power Systems Research November 2024 236
