Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design
This work addresses instability issues in convex regression for geometric programming-based circuit design, representing an incremental improvement with domain-specific applications.
The paper tackled the instability of piecewise linear convex regression methods in optimization contexts by applying ensemble methods like bagging, smearing, and random partitioning, and demonstrated their effectiveness in device modeling and constraint approximation for circuit design.
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.