activity
20162022
most citedLeveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition

14 citations · 31 across the 10 of their papers we have counts for

collaborators

16 papers

cond-mat.str-el202213 cited

Order-Disorder Transitions in (CaSr)RhSn

Puspa Upreti, Matthew Krogstad, Charlotte Haley +6

The classification of structural phase transitions as displacive or order-disorder in character is usually based on spectroscopic data above the transition. We use single crystal x…

math.OC2022

Condensed interior-point methods: porting reduced-space approaches on GPU hardware

François Pacaud, Sungho Shin, Michel Schanen +2

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…

math.OC202114 cited

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…

stat.ME2020

Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data

Christopher J. Geoga, Mihai Anitescu, Michael L. Stein

Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of ha…

physics.comp-ph20201 cited

A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities

Vishwas Rao, Romit Maulik, Emil Constantinescu +1

We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excurs…

math.OC2020

Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models

Daniel Adrian Maldonado, Vishwas Rao, Mihai Anitescu +1

In this paper we focus on the parameter estimation of dynamic load models with stochastic terms, in particular, load models where protection settings are uncertain, such as in aggr…