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math.OC2025

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-…

math.OC2025

An Augmented Lagrangian Method on GPU for Security-Constrained AC Optimal Power Flow

François Pacaud, Armin Nurkanović, Anton Pozharskiy +2

We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangi…

math.OC2025

Modeling and Optimization of Control Problems on GPUs

Alexis Montoison, Jean-Baptiste Caillau

We present a fully Julia-based, GPU-accelerated workflow for solving large-scale sparse nonlinear optimal control problems. Continuous-time dynamics are modeled and then discretize…

math.OC2025

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…

math.OC2025

Recovering sparse DFT from missing signals via interior point method on GPU

Wei Kuang, Alexis Montoison, Vishwas Rao +2

We propose a method to recover the sparse discrete Fourier transform (DFT) of a signal that is both noisy and potentially incomplete, with missing values. The problem is formulated…