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RL-赵-(六):随机逼近与随机梯度下降02-2:Robbins-Monro(RM)算法【wₖ₊₁=wₖ-αₖĝ(wₖ,ηₖ)】【SA领域的开创性工作】

发布时间:2026/9/24 6:47:27 来源:尧图企业网站定制
2、Robbins-Monro(RM)算法Robbins-Monro(RM)算法:wk+1 = wk − ak g~(wk,ηk ),k = 1,2,3,... \begin{aligned}w_{k+1}\:=\:w_k\:-\:a_k\:\tilde{g}(w_k,\eta_k\:),k\:=\:1,2,3,...\end{aligned}wk+1​=wk​−ak​g~​(wk​,ηk​),k=1,2,3,...​其中wkw_kwk​是root的第kkk次估计;g~(wk,ηk)=g(wk)+ηk\tilde{g}\left(w_k,\eta_k\right)=g(w_k)+\eta_kg~​(wk​,ηk​)=g(wk​)+ηk​是第kkk次带有噪声的观测;

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