Evaluation complexity bounds for smooth constrained nonlinear optimization using scaled KKT conditions and high-order models

Evaluation complexity for convexly constrained optimization is considered and it is shown first that the complexity bound of O(ε- ^−3∕2 ) proved by Cartis et al. (IMA J Numer Anal 32:1662–1695, 2012) for computing an ε-approximate first-order critical point can be obtained under significantly weaker assumptions. Moreover, the result is generalized to the case where high-order derivatives are used, resulting in a bound of (Formula presented) evaluations whenever derivatives of order p are available. It is also shown that the bound of (Formula presented) evaluations (ε- _P and ε- _D being primal and dual accuracy thresholds) suggested by Cartis et al. (SIAM J. Numer. Anal. 53:836–851, 2015) for the general nonconvex case involving both equality and inequality constraints can be generalized to yield a bound of (Formula presented) evaluations under similarly weakened assumptions.