Online averaged stochastic gradient algorithms are more and more studied since (i) they can deal quickly with large sample taking values in high dimensional spaces, (ii) they enable to treat data sequentially, (iii) they are known to be asymptotically efficient. In this paper, we focus on giving explicit bounds of the quadratic mean error of the estimates, and this, with very weak assumptions, i.e without supposing that the function we would like to minimize is strongly convex or admits a bounded gradient.

翻译：在线平均随机梯度算法研究得越来越多,因为(一) 它们可以快速处理在高维空间采集的大型样本值,(二) 它们能够按顺序处理数据,(三) 它们已知是轻微有效的。 在本文中,我们侧重于给出四边形估计中中误差的清晰界限,而这个假设非常薄弱,即不假设我们想要尽量减少的功能是强烈的二次曲线,或承认一个交界梯度。