Ergodic convergence of a stochastic proximal point algorithm
نویسنده
چکیده
The purpose of this paper is to establish the almost sure weak ergodic convergence of a sequence of iterates (xn) given by xn+1 = (I + λnA(ξn+1, . )) (xn) where (A(s, . ) : s ∈ E) is a collection of maximal monotone operators on a separable Hilbert space, (ξn) is an independent identically distributed sequence of random variables on E and (λn) is a positive sequence in l\l. The weighted averaged sequence of iterates is shown to converge weakly to a zero (assumed to exist) of the Aumann expectation E(A(ξ1, . )) under the assumption that the latter is maximal. We consider applications to stochastic optimization problems of the form minE(f(ξ1, x)) w.r.t. x ∈ m ⋂
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ورودعنوان ژورنال:
- SIAM Journal on Optimization
دوره 26 شماره
صفحات -
تاریخ انتشار 2016