  We consider chance-constrained problems with discrete random distribution. We
aim for problems with a large number of scenarios. We propose a novel method
based on the stochastic gradient descent method which performs updates of the
decision variable based only on considering a few scenarios. We modify it to
handle the non-separable objective. Complexity analysis and a comparison with
the standard (batch) gradient descent method is provided. We give three
examples with non-convex data and show that our method provides a good solution
fast even when the number of scenarios is large.
