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Worst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization

Frank E Curtis (frank.e.curtis***at***lehigh.edu)
Michael J O'Neill (moneill***at***lehigh.edu)
Daniel P Robinson (daniel.p.robinson***at***lehigh.edu)

Abstract: A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving optimization problems involving a stochastic objective function and deterministic nonlinear equality constraints. Barring additional terms that arise due to the adaptivity of the monotonically nonincreasing merit parameter sequence, the proved complexity bound is comparable to that known for the stochastic gradient algorithm for unconstrained nonconvex optimization. The overall complexity bound, which accounts for the adaptivity of the merit parameter sequence, shows that a result comparable to the unconstrained setting (with additional logarithmic factors) holds with high probability.

Keywords: nonlinear optimization, stochastic optimization, sequential quadratic optimization, worst-case complexity

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Category 2: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 12/29/2021
Entry Accepted: 12/29/2021
Entry Last Modified: 01/06/2022

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