An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians
This work addresses optimization problems with noisy data for researchers in computational mathematics and machine learning, representing an incremental improvement by adapting existing SQP methods to stochastic settings.
The paper tackles nonlinear optimization with stochastic objectives and deterministic constraints by proposing an adaptive stochastic sequential quadratic programming (SQP) algorithm using a differentiable exact augmented Lagrangian, establishing global convergence and demonstrating superiority in numerical experiments on the CUTEst test set.
We consider solving nonlinear optimization problems with a 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 simplify an old SQP method \citep{Lucidi1990Recursive} developed for solving deterministic problems, which serves as the skeleton of our stochastic algorithm. Based on the simplified deterministic algorithm, we then propose a non-adaptive SQP for dealing with stochastic objective, where the gradient and Hessian are replaced by stochastic estimates but the stepsizes are deterministic and prespecified. Finally, we incorporate a recent stochastic line search procedure \citep{Paquette2020Stochastic} into the non-adaptive stochastic SQP to adaptively select the random stepsizes, which leads to an adaptive stochastic SQP. The global "almost sure" convergence for both non-adaptive and adaptive SQP methods is established. Numerical experiments on nonlinear problems in CUTEst test set demonstrate the superiority of the adaptive algorithm.