mlsanityData to 7 Oct 2026

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

arXiv 2112.14799 ↗First posted 29 Dec 2021math.OCcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Dec 2021.

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Citing papers per year: 2022: 3; 2023: 5; 2024: 4; 2025: 5; 2026: 2.

19 papers.

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  1. 2022Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problemscited by 21
  2. 2023Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraintscited by 16
  3. 2023Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimizationcited by 11
  4. 2024Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guaranteescited by 11
  5. 2022Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programmingcited by 10
  6. 2023A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimizationcited by 8
  7. 2023A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problemscited by 8
  8. 2023Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimizationcited by 5
  9. 2024Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Modelscited by 5
  10. 2025Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraintscited by 5
  11. 2025High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noisecited by 4
  12. 2024Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimizationcited by 1
7 more
  1. 2026A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Methodcited by 1
  2. 2022DRSOM: A Dimension Reduced Second-Order Methodcited by 0
  3. 2024Efficient Second-Order Neural Network Optimization via Adaptive Trust Region Methodscited by 0
  4. 2025Mirror Descent Linearized Augmented Lagrangian Methods for Nonconvex Constrained Stochastic Zeroth-Order Optimizationcited by 0
  5. 2025Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networkscited by 0
  6. 2025humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problemscited by 0
  7. 2026MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method for Nonconvex Constrained DC-Regularized Optimizationcited by 0