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：为解决非线性平等受限制的随机优化问题,建议采用一步步搜索连续二次编程方法,假定存在制约功能值和衍生物,但只能通过不精确的概率零位和第一阶或手法来计算目标函数及其相关衍生物的随机近似值。根据合理的假设,可以得出一种高概率,即算法的迭代复杂性,以大致达到一级固定性。标准非线性优化测试的数值结果说明了我们拟议方法的优点和局限性。