AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
A line-search-free and function-value-free adaptive projected-gradient algorithm for the sample-average approximation (SAA) problem that transfers vanishing SAA residuals to Pareto stationarity for the population problem, while an additional concentration argument gives a finite-sample residual bound on compact sets.
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.
We develop accelerated gradient-free methods for stochastic convex optimization with constraints defined by expectations. Our batched primal-dual sliding method uses two-point evaluations sharing a random sample and guarantees expected objective error and expected maximum constraint violation at most $\varepsilon$. It...
We study nonsmooth convex-concave saddle-point problems over compact convex sets, assuming access to stochastic subgradients of the payoff function. We develop single-loop projection-free algorithms that use linear minimization oracles over the primal and dual domains. Unlike prior projection-free approaches that rely...
This work proposes a fully Hessian-inverse-free stochastic gradient method for SMOBO which is computationally efficient due to the use of a decoupling approach, and employs a momentum-like gradient estimate to replace the stochastic gradients with their moving average, thereby mitigating the errors induced by dynamic w...
A comprehensive complexity theory for the max-$M$ non-monotone direct-search in both deterministic and stochastic DFO problems is developed, enabled by a new family of merit functions that correct the stored objective values by ordered multiples of the squared stepsize.
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