The obtained results broaden the applicability of stochastic finite-difference methods to nonsmooth quasiconvex optimization problems and provide a rigorous theoretical justification of the algorithm in black-box settings where only noisy function evaluations are available.
The obtained results significantly broaden the applicability of stochastic finite-difference methods to nonsmooth quasiconvex optimization problems and provide a rigorous theoretical justification of the algorithm in black-box settings where only noisy function evaluations are available.
Rafik A. Khachatryan· Russian Universities Reports...· 0 citations
This paper proposes a novel decentralized stochastic first-order optimization algorithm, which does not require second-order Hessian or Jacobian matrices, for the setting where the lower-level loss function is nonconvex but satisfies the Polyak–Łojasiewicz (PL) condition.
Yihan Zhang, Xinwen Zhang, My T. Thai et al.· 0 citations
We consider the design of optimal fixed-step first-order methods for $M$-Lipschitz convex optimization given $\|x_0-x_\star\|\leq D$. Prior works have identified several distinct fixed-step methods, parameterized by a matrix of stepsizes $W$, with the (information-theoretic) minimax optimal rate $MD/\sqrt{N+1}$ of obje...
A simple compact-restriction principle for applying conditional gradient steps to unbounded feasible regions and records constructive restrictions based on strong convexity, exact sublevel sets, and epigraph caps.
This work introduces a proximal stochastic subgradient method for minimizing the sum of an expected cost, whose integrand is potentially nonsmooth and nonconvex, and a lower semicontinuous, prox-bounded function. We target a broad class of integrands obeying a nonsmooth, localized variant of the descent lemma in the de...
Felipe Atenas, Alejandro Jofré, Pedro Pérez-Aros et al.· 0 citations
This paper establishes last-iterate convergence of stochastic first-order methods for constrained smooth convex--concave minimax optimization under the standard bounded-variance stochastic oracle and establishes two types of convergence guarantees.
Tao-Li Zheng, Jiajin Li, Anthony Man-Cho So· 1 citation· ⚡1
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