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Bibliographic Details
Main Authors: Oztoprak, Figen, Byrd, Richard
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.14368
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author Oztoprak, Figen
Byrd, Richard
author_facet Oztoprak, Figen
Byrd, Richard
contents We propose a sequential quadratic programming (SQP) algorithm for inequality constrained optimization that is robust to the presence of bounded noise in function and derivative evaluations. We cover the case where constraint evaluations contain noise as well as the objective. The proposed algorithm is a line search SQP method with relaxations to deal with noise. We study the effect of noise on the global convergence behavior of the algorithm. We implement the algorithm with noise-aware quasi-Newton updates, and numerically observe that the algorithm can achieve accuracy proportional to the noise level and problem-dependent parameters, as suggested by the theory.
format Preprint
id arxiv_https___arxiv_org_abs_2604_14368
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A Noise Tolerant SQP Algorithm for Inequality Constrained Optimization
Oztoprak, Figen
Byrd, Richard
Optimization and Control
We propose a sequential quadratic programming (SQP) algorithm for inequality constrained optimization that is robust to the presence of bounded noise in function and derivative evaluations. We cover the case where constraint evaluations contain noise as well as the objective. The proposed algorithm is a line search SQP method with relaxations to deal with noise. We study the effect of noise on the global convergence behavior of the algorithm. We implement the algorithm with noise-aware quasi-Newton updates, and numerically observe that the algorithm can achieve accuracy proportional to the noise level and problem-dependent parameters, as suggested by the theory.
title A Noise Tolerant SQP Algorithm for Inequality Constrained Optimization
topic Optimization and Control
url https://arxiv.org/abs/2604.14368