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Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems …
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Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition
We study the convergence of accelerated stochastic gradient descent for strongly convex objectives under the growth condition, which …
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A Fast Temporal Decomposition Procedure for Long-horizon Nonlinear Dynamic Programming
We propose a fast temporal decomposition procedure for solving long-horizon nonlinear dynamic programs. The core of the procedure is …
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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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Superconvergence of Online Optimization for Model Predictive Control
We develop a one-Newton-step-per-horizon, online, lag-$L$, model predictive control (MPC) algorithm for solving discrete-time, …
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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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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 …
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An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for …
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Global Convergence of Online Optimization for Nonlinear Model Predictive Control
We study a real-time iteration (RTI) scheme for solving online optimization problem appeared in nonlinear optimal control. The proposed …
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High-dimensional Index Volatility Models via Stein's Identity
We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order …
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