AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
This work introduces a nonlinear Hager-Zhang conjugate gradient method for solving set optimization problems. The objective function under consideration is defined by a finite collection of continuously differentiable functions. Notably, the proposed approach imposes restrictions neither on the existence of a finite ge...
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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