Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity

30 Jul 2019Feihu HuangShangqian GaoHeng Huang

Zeroth-order method is a class of powerful optimization tool for many machine learning problems because it only needs function values (not gradient) in the optimization. Recently, although many zeroth-order methods have been developed, these approaches still exist one of two main drawbacks: 1) high function query complexity; 2) not being well suitable for solving the problems with complex penalties and constraints... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper