  Convex regression is a promising area for bridging statistical estimation and
deterministic convex optimization. New piecewise linear convex regression
methods are fast and scalable, but can have instability when used to
approximate constraints or objective functions for optimization. Ensemble
methods, like bagging, smearing and random partitioning, can alleviate this
problem and maintain the theoretical properties of the underlying estimator. We
empirically examine the performance of ensemble methods for prediction and
optimization, and then apply them to device modeling and constraint
approximation for geometric programming based circuit design.
