Arielle Carr - Papers and Preprints

Most of the works listed below have drafts available at provided links. Please email me if a link is broken and/or you do not have access to a work you are interested in reading. Please also see my Google Scholar page for the most current information on my publications.

Selected Publications and Preprints

David Millard, Arielle Carr, Stéphane Gaudreault, Ali Baheri. PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning. Submitted December 2024. Preprint available here.
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Brandon Augustino, Arielle Carr, Tamás Terlaky. Improvements to quantum interior point method for linear optimization. ACM Transactions on Quantum Computing (2024). Preprint available here.
Yixin Liu, Arielle Carr, Lichao Sun. Empirical Perturbation Analysis of Linear System Solvers from a Data Poisoning Perspective. Submitted September 2024. Preprint available here.
David Millard, Arielle Carr, Stéphane Gaudreault. Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics. 2024 IEEE International Conference on Big Data (BigData 2024, to appear). Preprint available here.
David Millard, Arielle Carr, Stéphane Gaudreault. Data-Driven Initial Guess Selection for Numerical Weather Prediction Solvers. 2024 IEEE International Conference on Big Data (BigData 2024, to appear). Preprint available here.
Yuesheng Xu, Arielle Carr. A Dynamic Weighting Strategy to Mitigate Worker Node Failure in Distributed Deep Learning. 28th Annual IEEE High Performance Extreme Computing Conference (2024, to appear). Preprint available here.
Rishad Islam, Arielle Carr, Colin Jacobs. Optimization of Approximate Maps for Linear Systems Arising in Discretized PDEs. Proceedings in Applied Mathematics and Mechanics (to appear). Preprint available here.
Stephen Thomas, Arielle Carr, Paul Mullowney, Kasia Świrydowicz, Marc Day. Scaled ILU Smoothers for Navier-Stokes Pressure Projection. International Journal for Numerical Methods in Fluids (2024). Available here.
Stephen Thomas, Erin Carson, Miro Rozlozik, Arielle Carr, Kasia Świrydowicz. Iterated Gauss-Seidel GMRES. SIAM Journal on Scientific Computing (2024). Available here.
Arielle Carr, Eric de Sturler, Mark Embree. Analysis of GMRES for Low-Rank and Small-Norm Perturbations of the Identity Matrix. Proceedings in Applied Mathematics and Mechanics (2023). Available here.
Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Brandon Augustino, Arielle Carr, Tamás Terlaky. Quantum-enhanced Regression Analysis using State-of-the-art QLSAs and QIPMs. ACM/IEEE Workshop on Quantum Computing (2022).
Eric Enouen, Katja Mathesius, Sean Wang, Arielle Carr, Sihong Xie. Efficient First-Order Predictor-Corrector Multiple Objective Optimization for Fair Misinformation Detection. 2022 IEEE International Conference on Big Data (BigData 2022). Preprint available: arXiv preprint arXiv:2209.07245.
Sean Wang, Arielle Carr, Sihong Xie. A Predictor-Corrector Method for Multi-objective Optimization in Fair Machine Learning. REU Symposium, 2022 IEEE/ACM International Conference on Big Data Computing, Applications, and Technologies (BDCAT 2022).
Arielle Carr, Eric de Sturler, Serkan Gugercin. Preconditioning Parametrized Linear Systems SIAM Journal on Scientific Computing (2021). Available here.
Arielle Carr, Eric de Sturler. A Warm-Started Krylov-Schur Method for Solving Sequences of Eigenproblems. (In prepartion - A draft version of the full paper is available in my thesis, see Chapter 3.)

Chapters

Arielle Carr. "The Power of Productive Struggle." in Teaching Gradually: Practical Pedagogy for Graduate Students, by Graduate Students (2021). (Password protected pdf available here.)