A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
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Abstract (arXiv)
In this paper, we propose a trust-region interior-point stochastic sequential quadratic programming (TR-IP-SSQP) method for solving optimization problems with a stochastic objective and deterministic nonlinear equality and inequality constraints. In this setting, exact evaluations of the objective function and its gradient are unavailable, but their stochastic estimates can be constructed. In particular, at each iteration our method builds stochastic oracles, which estimate the objective value and gradient to satisfy proper adaptive accuracy conditions with a fixed probability. To handle inequality constraints, we adopt an interior-point method (IPM), in which the barrier parameter follows a prescribed decaying sequence. Under standard assumptions, we establish global almost-sure convergence of the proposed method to first-order stationary points. We implement the method on a subset of problems from the CUTEst test set, as well as on logistic regression problems, to demonstrate its practical performance.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 10 Mar 2026.
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23 of 43 references.
References · 23
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11 more
- 2022First- and Second-Order High Probability Complexity Bounds for Trust-Region Methods with Noisy Oraclescited by 8
- 2023A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimizationcited by 8
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- 2023A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problemscited by 8
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- 2025High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noisecited by 4
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- 2025An Interior-Point Algorithm for Continuous Nonlinearly Constrained Optimization with Noisy Function and Derivative Evaluationscited by 1
- 2025Retrospective Approximation Sequential Quadratic Programming for Stochastic Optimization with General Deterministic Nonlinear Constraintscited by 1
- 2025Stochastic Adaptive Optimization with Unreliable Inputs: A Unified Framework for High-Probability Complexity Analysiscited by 1
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Citing papers per year: 2026: 2.
2 papers.