Fast gradient descent for convex minimization problems with an oracle producing a \(( \delta, L)\)-model of function at the requested point

DOI10.1134/S0965542519070078OpenAlexW2968762515MaRDI QIDQ2278192

A. I. Tyurin, Alexander V. Gasnikov

Publication date: 4 December 2019

Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0965542519070078

zbMATH Keywords

gradient descentconditional gradient methodcomposite optimizationuniversal methodfast gradient descentmodel of function

Mathematics Subject Classification ID

Numerical analysis (65-XX) Operations research, mathematical programming (90-XX)

Related Items (10)

First-order methods for convex optimization ⋮ Unifying framework for accelerated randomized methods in convex optimization ⋮ A heuristic adaptive fast gradient method in stochastic optimization problems ⋮ Accelerated and unaccelerated stochastic gradient descent in model generality ⋮ Accelerated methods for saddle-point problem ⋮ Analogues of Switching Subgradient Schemes for Relatively Lipschitz-Continuous Convex Programming Problems ⋮ Solving convex min-min problems with smoothness and strong convexity in one group of variables and low dimension in the other ⋮ Inexact model: a framework for optimization and variational inequalities ⋮ Universal intermediate gradient method for convex problems with inexact oracle ⋮ Convex optimization with inexact gradients in Hilbert space and applications to elliptic inverse problems

Uses Software

• PESTO
• NESUN

Cites Work

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