Some worst-case datasets of deterministic first-order methods for solving binary logistic regression

DOI10.3934/ipi.2020047zbMath1469.90090arXiv1908.04091OpenAlexW3047706078MaRDI QIDQ2028922

Yuyuan Ouyang, Trevor Squires

Publication date: 3 June 2021

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.04091

zbMATH Keywords

nonlinear optimizationinformation-based complexitylower complexity boundfirst-order methodsbinary logistic regression

Mathematics Subject Classification ID

Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30)

Cites Work

• Unnamed Item
• On lower complexity bounds for large-scale smooth convex optimization
• The exact information-based complexity of smooth convex minimization
• Information-based complexity of linear operator equations
• Introductory lectures on convex optimization. A basic course.
• Self-concordant analysis for logistic regression
• Lower bounds for finding stationary points I
• Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems
• Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization

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