44 citations · 250 across the 61 of their papers we have counts for
68 papers
Control and Estimation Co-Design via Envelope-Theorem Gradients
Mohammad S. Ramadan, Philip Dinenis, Mihai Anitescu
Instead of the sequential plant--control--estimator design pipeline, which is in general not optimal, we propose the Control and Estimation Co-Design (ContEst) framework, which pos…
ExaModels.jl: an Algebraic Modeling System for Nonlinear Programming on GPUs
Sungho Shin, Michel Schanen, François Pacaud +2
Large-scale nonlinear programs almost always exhibit partially separable and repetitive structure, yet most existing algebraic modeling systems do not take advantage of it. A nonli…
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…
Overlapping Schwarz Scheme for Linear-Quadratic Programs in Continuous Time
Hongli Zhao, Mihai Anitescu, Sen Na
We present an optimize-then-discretize framework for solving linear-quadratic optimal control problems (OCP) governed by time-inhomogeneous ordinary differential equations (ODEs).…
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-…
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…
BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
Efficient matrix trace estimation is essential for scalable computation of log-determinants, matrix norms, and distributional divergences. In many large-scale applications, the mat…
Weakly-Constrained 4D Var for Downscaling with Uncertainty using Data-Driven Surrogate Models
Philip Dinenis, Vishwas Rao, Mihai Anitescu
Dynamic downscaling typically involves using numerical weather prediction (NWP) solvers to refine coarse data to higher spatial resolutions. Data-driven models such as FourCastNet…
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…
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…
Floodgates up to contain the DeePC and limit extrapolation
Mohammad Ramadan, Evan Toler, Mihai Anitescu
Behavioral data-enabled control approaches typically assume data-generating systems of linear dynamics. This may result in false generalization if the newly designed closed-loop sy…
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…
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…
Data-conforming data-driven control: avoiding premature generalizations beyond data
Mohammad Ramadan, Evan Toler, Mihai Anitescu
Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, the…
The statistical spread of transmission outages on a fast protection time scale based on utility data
Ian Dobson, D. Adrian Maldonado, Mihai Anitescu
When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in…
A Framework for Balancing Power Grid Efficiency and Risk with Bi-objective Stochastic Integer Optimization
Ramsey Rossmann, Mihai Anitescu, Julie Bessac +4
Power grid expansion planning requires making large investment decisions in the present that will impact the future cost and reliability of a system exposed to wide-ranging uncerta…
Scalable Multi-Period AC Optimal Power Flow Utilizing GPUs with High Memory Capacities
Sungho Shin, Vishwas Rao, Michel Schanen +2
This paper demonstrates the scalability of open-source GPU-accelerated nonlinear programming (NLP) frameworks -- ExaModels.jl and MadNLP.jl -- for solving multi-period alternating…
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…
Extended Kalman filter -- Koopman operator for tractable stochastic optimal control
Mohammad S. Ramadan, Mihai Anitescu
The theory of dual control was introduced more than seven decades ago. Although it has provided rich insights to the fields of control, estimation, and system identification, dual…
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…
Data-Driven Estimation of Failure Probabilities in Correlated Structure-Preserving Stochastic Power System Models
Hongli Zhao, Tyler E. Maltba, D. Adrian Maldonado +2
We propose a data-driven approach for propagating uncertainty in stochastic power grid simulations and apply it to the estimation of transmission line failure probabilities. A redu…
Voltage-Dependent Electromechanical Wave Propagation Modeling for Dynamic Stability Analysis in Power Systems
Somayeh Yarahmadi, Daniel Adrian Maldonado, Lamine Mili +2
Accurate dynamic modeling of power systems is essential to assess the stability of electrical power systems when faced with disturbances, which can trigger cascading failures leadi…
Centralized calibration of power system dynamic models using variational data assimilation
Ahmed Attia, D. Adrian Maldonado, Emil Constantinescu +1
This paper presents a novel centralized, variational data assimilation approach for calibrating transient dynamic models in electrical power systems, focusing on load model paramet…
Accelerating Optimal Power Flow with GPUs: SIMD Abstraction of Nonlinear Programs and Condensed-Space Interior-Point Methods
Sungho Shin, François Pacaud, Mihai Anitescu
This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkabl…