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This paper investigates stochastic multi-level optimization where the objective is a nested composition of several smooth non-convex functions. We assume that only stochastic estimates of the gradient and function values for each level are accessible. Consequently, obtaining an accurate estimate of the overall gradient...
This paper solves the problem of sign-based variance reduction methods' failure to obtain optimal convergence rates for both nonconvex stochastic and finite-sum optimization, and proposes tracking the global gradient at the server through unbiased compression of recursive gradient increments.
This paper studies projection-free algorithms for stochastic constrained multi-level compositional optimization and proposes variance-reduced projection-free algorithms and establishes complexity guarantees under both the Frank-Wolfe gap and the gradient mapping criteria.
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