## A Primal-Dual Decomposition Algorithm for Multistage Stochastic Convex Programming (2000)

Venue: | Convex Programming, Mathematical Programming 104, 153 |

Citations: | 7 - 3 self |

### BibTeX

@TECHREPORT{Berkelaar00aprimal-dual,

author = {Arjan Berkelaar and Roy Kouwenberg and Shuzhong Zhang},

title = {A Primal-Dual Decomposition Algorithm for Multistage Stochastic Convex Programming},

institution = {Convex Programming, Mathematical Programming 104, 153},

year = {2000}

}

### OpenURL

### Abstract

This paper presents a new and high performance solution method for multistage stochastic convex programming. Stochastic programming is a quantitative tool developed in the field of optimization to cope with the problem of decision-making under uncertainty. Among others, stochastic programming has found many applications in finance, such as asset-liability and bond-portfolio management. However, many stochastic programming applications still remain computationally intractable because of their overwhelming dimensionality. In this paper we propose a new decomposition algorithm for multistage stochastic programming with a convex objective, based on the path-following interior point method combined with the homogeneous self-dual embedding technique. Our preliminary numerical experiments show that this approach is very promising in many ways for solving generic multistage stochastic programming, including its superiority in terms of numerical e#ciency, as well as the flexibility in testing and analyzing the model.