# A Sequential Quadratic Programming Method for Optimization with Stochastic Objective Functions, Deterministic Inequality Constraints and Robust Subproblems

arXiv: https://arxiv.org/abs/2302.07947 · HTML: https://mlsanity.com/paper/2302.07947/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2023.

## Built on by (most cited first, 8 of 8)

- [Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guarantees](https://mlsanity.com/paper/2409.09906/) (2024)
- [A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems](https://mlsanity.com/paper/2304.14907/) (2023)
- [Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization](https://mlsanity.com/paper/2308.03687/) (2023)
- [Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models](https://mlsanity.com/paper/2409.15734/) (2024)
- [High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noise](https://mlsanity.com/paper/2503.19091/) (2025)
- [A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method](https://mlsanity.com/paper/2603.10230/) (2026)
- [Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization](https://mlsanity.com/paper/2408.16186/) (2024)
- [The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming](https://mlsanity.com/paper/2402.14844/) (2024)

## Abstract (arXiv)

In this paper, a robust sequential quadratic programming method for constrained optimization is generalized to problem with an {expectation} objective function {and} deterministic equality and inequality constraints. A stochastic line search scheme is employed to globalize the steps. {We show theoretically that sequences generated by the algorithm converge almost surely to a Karush-Kuhn-Tucker point under the assumption of the extended Mangasarian-Fromovitz constraint qualification}. Encouraging numerical results are reported.