Gradient and Hessian of Joint Probability Function with Applications on Chance-Constrained Programs
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › Not applicable › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 431-455 |
| Journal / Publication | Journal of the Operations Research Society of China |
| Volume | 5 |
| Issue number | 4 |
| Early online date | 13 Mar 2017 |
| Publication status | Published - Dec 2017 |
Link(s)
Abstract
Joint probability function refers to the probability function that requires multiple conditions to satisfy simultaneously. It appears naturally in chance-constrained programs. In this paper, we derive closed-form expressions of the gradient and Hessian of joint probability functions and develop Monte Carlo estimators of them. We then design a Monte Carlo algorithm, based on these estimators, to solve chance-constrained programs. Our numerical study shows that the algorithm works well, especially only with the gradient estimators.
Research Area(s)
- Chance-constrained program, Gradient estimation, Monte Carlo simulation
Citation Format(s)
Gradient and Hessian of Joint Probability Function with Applications on Chance-Constrained Programs. / Hong, L. Jeff; Jiang, Guang-Xin.
In: Journal of the Operations Research Society of China, Vol. 5, No. 4, 12.2017, p. 431-455.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › Not applicable › peer-review
