  We consider the problem of maximizing a non-concave Lipschitz multivariate
function over a compact domain by sequentially querying its (possibly
perturbed) values. We study a natural algorithm designed originally by
Piyavskii and Shubert in 1972, for which we prove new bounds on the number of
evaluations of the function needed to reach or certify a given optimization
accuracy. Our analysis uses a bandit-optimization viewpoint and solves an open
problem from Hansen et al.\ (1991) by bounding the number of evaluations to
certify a given accuracy with a near-optimal sum of packing numbers.

Machine Learning;Optimization and Control;Machine Learning