No dimension-free deterministic algorithm computes approximate stationarities of Lipschitzians

Lai Tian, Anthony Man-Cho So

Abstract: We consider the oracle complexity of computing an approximate stationary point of a Lipschitz function. When the function is smooth, it is well known that the simple deterministic gradient method has finite dimension-free oracle complexity. However, when the function can be nonsmooth, it is only recently that a randomized algorithm with finite dimension-free oracle complexity has been developed. In this paper, we show that no deterministic algorithm can do the same. Moreover, even without the dimension-free requirement, we show that any finite-time deterministic method cannot be general zero-respecting. In particular, this implies that a natural derandomization of the aforementioned randomized algorithm cannot have finite-time complexity. Our results reveal a fundamental hurdle in modern large-scale nonconvex nonsmooth optimization.