# An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians

@article{Na2021AnAS, title={An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians}, author={Sen Na and Mihai Anitescu and Mladen Kolar}, journal={ArXiv}, year={2021}, volume={abs/2102.05320} }

We consider solving nonlinear optimization problems with stochastic objective and deterministic equality constraints. We assume for the objective that its evaluation, gradient, and Hessian are inaccessible, while one can compute their stochastic estimates by, for example, subsampling. We propose a stochastic algorithm based on sequential quadratic programming (SQP) that uses a differentiable exact augmented Lagrangian as the merit function. To motivate our algorithm design, we first revisit and…

## 16 Citations

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The proposed sequential quadratic optimization algorithm both allows the use of stochastic objective gradient estimates and possesses convergence guarantees even in the setting in which the constraint Jacobians may be rank deficient.

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A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, analyzed, and tested and is proved to possess convergence guarantees in expectation.

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