Normalized-step stochastic SQP methods for nonconvex constrained optimization under heavy-tailed noise
In this paper, we study nonconvex constrained optimization with expectation equality constraints and non-negative bounds under heavy-tailed noise. The objective gradients, constraint values, and constraint Jacobian are accessed through stochastic oracles whose errors have bounded $p$th central moments for some $p\in(1,2)$. These conditions allow infinite-variance errors for the three types of stochastic oracles, rendering traditional … Read more