Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization

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.