Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization

A. Ali, Z. Kolter, S. Diamond, and S. Boyd

Proceedings of 31st Conference on Uncertainty in Artificial Intelligence, 2015.

We introduce disciplined convex stochastic programming (DCSP), a modeling framework that can significantly lower the barrier for modelers to specify and solve convex stochastic optimization problems, by allowing modelers to naturally express a wide variety of convex stochastic programs in a manner that reflects their underlying mathematical representation. DCSP allows modelers to express expectations of arbitrary expressions, partial optimizations, and chance constraints across a wide variety of convex optimization problem families (e.g., linear, quadratic, second order cone, and semidefinite programs). We illustrate DCSP's expressivity through a number of sample implementations of problems drawn from the operations research, finance, and machine learning literatures.