The adaptive regularization algorithm for unconstrained nonconvex

optimization was shown in Nesterov and Polyak (2006) and Cartis, Gould and Toint (2011) to require, under standard assumptions, at most O(\epsilon^{3/(3-q)}) evaluations of the objective function and its derivatives of degrees one and two to produce an \epsilon-approximate critical point of order q in {1,2}. This bound was shown to be sharp for q in {1,2} by various authors. This note revisits these results and shows that the example for which slow convergence is exhibited is not isolated, but that this behaviour occurs for a subset of univariate functions of nonzero measure.

## Article

View Examples of slow convergence for adaptive regularization optimization methods are not isolated