A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, but only stochastic approximations of the objective function and its associated derivatives can be computed via inexact probabilistic zeroth- and first-order oracles. Under reasonable assumptions, a high-probability bound on the iteration complexity of the algorithm to approximate first-order stationarity is derived. Numerical results on standard nonlinear optimization test problems illustrate the advantages and limitations of our proposed method.

翻译：本文提出了一种步长搜索序列二次规划方法，用于求解非线性等式约束随机优化问题。假设约束函数值及其导数可精确获取，但目标函数及其相关导数仅能通过不精确的概率零阶和一阶预言机计算其随机近似。在合理假设下，推导了算法逼近一阶驻点所需迭代次数的高概率复杂度界。在标准非线性优化测试问题上的数值结果阐明了所提方法的优势与局限性。