WIAS Preprint No. 1687, (2012)

A gradient formula for linear chance constraints under Gaussian distribution



Authors

  • Henrion, René
  • Möller, Andris

2010 Mathematics Subject Classification

  • 90C15

Keywords

  • Chance constraints, probabilistic constraints, derivative of singular normal distribution, derivative of Gaussian probability for polyhedra

Abstract

We provide an explicit gradient formula for linear chance constraints under a (possibly singular) multivariate Gaussian distribution. This formula allows one to reduce the calculus of gradients to the calculus of values of the same type of chance constraints (in smaller dimension and with different distribution parameters). This is an important aspect for the numerical solution of stochastic optimization problems because existing efficient codes for e.g., calculating singular Gaussian distributions or regular Gaussian probabilities of polyhedra can be employed to calculate gradients at the same time. Moreover, the precision of gradients can be controlled by that of function values which is a great advantage over using finite difference approximations. Finally, higher order derivatives are easily derived explicitly. The use of the obtained formula is illustrated for an example of a transportation network with stochastic demands.

Appeared in

  • Math. Oper. Res., 37 (2012) pp. 475--488.

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