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The Stochastic Frank–Wolfe (SFW) algorithm is proposed, a projection-free framework that leverages linear minimization oracles to bypass expensive projection operations and achieves rigorous convergence rate guarantees for both convex and non-convex objectives.
It is proved that the proposed step size preserves the descent property of the MCondG method, and has an improved worst-case complexity bound compared with the original adaptive step size strategy.
A momentum-based variance-reduced algorithm for federated multiobjective optimization that incorporates a momentum-driven gradient estimator into the local updates to reduce the variance of stochastic updates, leading to an improved convergence rate.
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