collaborators

16 papers

math.OC2026

Harnessing GPU Acceleration in Large-Scale Process Optimization

Boxun Huang, David Y. Shu, Michel Schanen +3

This paper presents a proof-of-concept workflow for equation-oriented process optimization that runs entirely on a GPU. Process optimization models often incorporate complex interc…

math.OC2026

Parallel Sequential Quadratic Programming with Overlapping Graph Decomposition and Exact Augmented Lagrangian

Runxin Ni, Haoxuan Wang, Sen Na +2

In this paper, we address the challenge of solving large-scale graph-structured nonlinear programs (gsNLPs) in a scalable manner. GsNLPs are problems in which the objective and con…

eess.SY2026

Simultaneous improvement of control and estimation for battery management systems

Mohammad S. Ramadan, Marfred Barrera, Mihai Anitescu +1

Standard battery management systems treat the control and state estimation problems as decoupled objectives, relying on certainty equivalence controllers that are blind to the vary…

stat.ML2026

Online Covariance Matrix Estimation in Sketched Newton Methods

Wei Kuang, Mihai Anitescu, Sen Na

Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods particularly standing out for their efficiency and…

eess.SY2026

Dampening parameter distributional shifts under robust control and gain scheduling

Mohammad Ramadan, Mihai Anitescu

Many traditional robust control approaches assume linearity of the system and independence between the system state-input and the parameters of its approximant (possibly lower-orde…

math.OC2026

Improved Approximation Bounds for Moore-Penrose Inverses of Banded Matrices with Applications to Continuous-Time Linear Quadratic Control

Sungho Shin, Wallace Gian Yion Tan, Mihai Anitescu

We present improved approximation bounds for the Moore-Penrose inverses of banded matrices, where the bandedness is induced by a metric on the index set. We show that the pseudoinv…