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Found 13 papers, 6 papers with code
Extended Kalman filter -- Koopman operator for tractable stochastic optimal control
It has been more than seven decades since the introduction of the theory of dual control \cite{feldbaum1960dual}.
Voltage-Dependent Electromechanical Wave Propagation Modeling for Dynamic Stability Analysis in Power Systems
Analyzing these characteristics enables the assessment of the impacts of EMW on the performance of the protection system.
Network Cascade Vulnerability using Constrained Bayesian Optimization
Measures of power grid vulnerability are often assessed by the amount of damage an adversary can exact on the network.
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization.
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Gaussian processes (GPs) are an attractive class of machine learning models because of their simplicity and flexibility as building blocks of more complex Bayesian models.
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems
This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting.
Inequality Constrained Stochastic Nonlinear Optimization via Active-Set Sequential Quadratic Programming
We study nonlinear optimization problems with a stochastic objective and deterministic equality and inequality constraints, which emerge in numerous applications including finance, manufacturing, power systems and, recently, deep neural networks.
Randomized Algorithms for Scientific Computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science.
An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
Based on the simplified deterministic algorithm, we then propose a non-adaptive SQP for dealing with stochastic objective, where the gradient and Hessian are replaced by stochastic estimates but the stepsizes are deterministic and prespecified.
Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs.
Stochastic Optimization
Optimization and Control
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs).
Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes
We present a distributionally robust formulation of a stochastic optimization problem for non-i. i. d vector autoregressive data.
A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming
It is substantially faster than the interior point method, and just as accurate.
Computation Methodology
