``Efficient” Subgradient Methods for General Convex Optimization

DOI10.1137/15M1027371zbMath1351.90129arXiv1605.08712MaRDI QIDQ5506689

James Renegar

Publication date: 13 December 2016

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1605.08712

zbMATH Keywords

convex optimizationsubgradient methodconic linear optimization

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Numerical optimization and variational techniques (65K10)

Related Items (14)

Exact penalties for decomposable convex optimization problems ⋮ A Level-Set Method for Convex Optimization with a Feasible Solution Path ⋮ New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure ⋮ A bound on the Carathéodory number ⋮ Radial duality. I: Foundations ⋮ Radial duality. II: Applications and algorithms ⋮ Radial Subgradient Method ⋮ A simple nearly optimal restart scheme for speeding up first-order methods ⋮ Accelerated first-order methods for hyperbolic programming ⋮ Amenable cones: error bounds without constraint qualifications ⋮ RSG: Beating Subgradient Method without Smoothness and Strong Convexity ⋮ Certifying Polynomial Nonnegativity via Hyperbolic Optimization ⋮ Faster subgradient methods for functions with Hölderian growth ⋮ Convergence Rates for Deterministic and Stochastic Subgradient Methods without Lipschitz Continuity

Cites Work

• Smooth minimization of non-smooth functions
• First-order algorithm with \({\mathcal{O}(\ln(1/\epsilon))}\) convergence for \({\epsilon}\)-equilibrium in two-person zero-sum games
• New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure
• Accelerated first-order methods for hyperbolic programming
• Convex Analysis

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