Special Session 129: Mathematics of Data Science and Applications

Convergence of zeroth-order optimization with stochastic mirror descent
Ting HU
Xi`an Jiaotong University
Peoples Rep of China
Co-Author(s):    
Abstract:
We study a randomized zeroth-order algorithm based only on computation of the difference of function values, which is associated with mirror descent to solve stochastic optimization problems. In the convex setting, we present a new convergence analysis of zeroth-order algorithm with general mirror maps, which relies on the $\ga$-H\"older $(0