We show how a technique from signal processing known as zero-delay convolution can be used to develop more efficient dynamic programming algorithms for a broad class of stochastic...
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
— Ergodic stochastic optimization (ESO) algorithms are proposed to solve resource allocation problems that involve a random state and where optimality criteria are expressed in t...
A new stochastic optimization algorithm referred to by the authors as the `Mean-Variance Optimization' (MVO) algorithm is presented in this paper. MVO falls into the category ...
Istvan Erlich, Ganesh K. Venayagamoorthy, Nakawiro...
The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to...