Math.OC

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 …
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Multiple-Shooting Interior-Point Differential Dynamic Programming for Constrained Nonlinear Optimal Control
We propose Multiple-Shooting Interior-Point Differential Dynamic Programming (MS-IPDDP) for finite-horizon constrained nonlinear …
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Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
Most first-order optimizers treat matrix-valued parameters as vectors, ignoring the intrinsic geometry of hidden-layer weights in …
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Inference of Online Newton Methods with Nesterov's Accelerated Sketching
Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order …
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A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
In this paper, we propose a trust-region interior-point stochastic sequential quadratic programming (TR-IP-SSQP) method for solving …
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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 …
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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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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. …
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Online Statistical Inference for Proximal Stochastic Gradient Descent under Markovian Sampling
Nonsmooth stochastic optimization has emerged as a fundamental framework for modeling complex machine learning problems, particularly …