随机约束下随机凸函数的极小化及其应用
Minimizing a stochastic convex function subject to stochastic constraints and some applications
摘要：
在最简单的情况下，我们得到了极小化从零到某个非负随机变量τ的非减右连续随机过程的积分问题的一般解，条件是对某个非负随机变量T，τ在零和T a. s之间.τ的期望值是某个α。允许非减过程和T是相依的。事实上，一个更一般的设置，涉及西格玛有限的措施，而不仅仅是概率测度被认为是家庭的随机过程的一些后果作为特殊情况。提供了各种应用。
英文摘要：
In the simplest case, we obtain a general solution to a problem of minimizing an integral of a nondecreasing right continuous stochastic process from zero to some nonnegative random variable tau, under the constraints that for some nonnegative random variable T, tau is between zero and T a.s. and the expected value of tau is some alpha. The nondecreasing process and T are allowed to be dependent. In fact a more general setup involving sigma-finite measures, rather than just probability measures is considered and some consequences for families of stochastic processes are given as special cases. Various applications are provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
