cs.LG

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 …
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Online Covariance Estimation in Nonsmooth Stochastic Approximation
We consider applying stochastic approximation (SA) methods to solve nonsmooth variational inclusion problems. Existing studies have …
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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 …
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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 …
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Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential …
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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 …
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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 …
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Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety …
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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 …
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Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence
We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the …
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