AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work shows that a simple denominator-only regularization of the DBGD multiplier eliminates the need for such an assumption while preserving fast convergence rates, and improves upon the best assumption-free complexities.
The method incorporates a scheme to determine the step size adaptively, by means of a local estimation of the smoothness constant, by means of a local estimation of the smoothness constant, without resorting to line search procedures.
An inertial system with Hessian-driven damping is developed, which complements existing results on smooth saddle point problems with bilinear coupling and considers the affinely constrained convex optimization problem.
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