Gradient Formulae for Nonlinear Probabilistic Constraints with Gaussian and Gaussian-Like Distributions

Abstract

Probabilistic constraints represent a major model of stochastic optimization. A possible approach for solving probabilistically constrained optimization problems consists in applying nonlinear programming methods. To do so, one has to provide sufficiently precise approximations for values and gradients of probability functions. For linear probabilistic constraints under Gaussian distribution this can be done successfully by analytically reducing these values and gradients to values of Gaussian distribution functions and computing the latter, for instance, by Genz's code. For nonlinear models one may fall back on the spherical-radial decomposition of Gaussian random vectors and apply, for instance, Deák's sampling scheme for the uniform distribution on the sphere in order to compute values of corresponding probability functions. The present paper demonstrates how the same sampling scheme can be used to simultaneously compute gradients of these probability functions. More precisely, we prove a formula representing these gradients in the Gaussian case as a certain integral over the sphere again. The result is also extended to alternative distributions with an emphasis on the multivariate Student's (or $t$-) distribution.

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