A stroll in the jungle of error bounds

Abstract

The aim of this paper is to give a short overview on error bounds and to provide the first bricks of a unified theory. Inspired by the works of [8, 15, 13, 16, 10], we show indeed the centrality of the Lojasiewicz gradient inequality. For this, we review some necessary and sufficient conditions for global/local error bounds, both in the convex and nonconvex case. We also recall some results on quantitative error bounds which play a major role in convergence rate analysis and complexity theory of many optimization methods.

References in corpus (1)

• Error Bounds for Parametric Polynomial Systems with Applications to Higher-Order Stability Analysis and Convergence Rates

Cited by in corpus (2)

• An algorithm to compute the Hoffman constant of a system of linear constraints
• Equivalence and invariance of the chi and Hoffman constants of a matrix