3

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
We study online statistical inference for the solutions of stochastic optimization problems with equality and inequality constraints. …
Cite arXiv
Derivative-Free Sequential Quadratic Programming for Equality-Constrained Stochastic Optimization
We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints, assuming that …
Cite arXiv
Overlapping Schwarz Scheme for Linear-Quadratic Programs in Continuous Time
We present an optimize-then-discretize framework for solving linear-quadratic optimal control problems (OCP) governed by …
Cite arXiv
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex …
Cite arXiv
High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise
In this paper, we consider nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We …
Cite arXiv
Online Covariance Matrix Estimation in Sketched Newton Methods
Given the ubiquity of streaming data, online algorithms have been widely used for parameter estimation, with second-order methods …
Cite arXiv
Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
In this work, we consider solving optimization problems with a stochastic objective and deterministic equality constraints. We propose …
Cite arXiv
Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
Physics-Informed Neural Networks (PINNs) represent a significant advancement in Scientific Machine Learning (SciML), which integrate …
Cite arXiv
Distributed Sequential Quadratic Programming with Overlapping Graph Decomposition and Exact Augmented Lagrangian
In this paper, we address the challenge of solving large-scale graph-structured nonlinear programs (gsNLPs) in a scalable manner. …
Cite arXiv