2 citations · 2 across the 4 of their papers we have counts for
5 papers
SCQPTH: an efficient differentiable splitting method for convex quadratic programming
Andrew Butler
We present SCQPTH: a differentiable first-order splitting method for convex quadratic programs. The SCQPTH framework is based on the alternating direction method of multipliers (AD…
Gradient boosting for convex cone predict and optimize problems
Andrew Butler, Roy H. Kwon
Prediction models are typically optimized independently from decision optimization. A smart predict then optimize (SPO) framework optimizes prediction models to minimize downstream…
Efficient differentiable quadratic programming layers: an ADMM approach
Andrew Butler, Roy Kwon
Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network. In…
Data-driven integration of norm-penalized mean-variance portfolios
Andrew Butler, Roy H. Kwon
Mean-variance optimization (MVO) is known to be sensitive to estimation error in its inputs. Norm penalization of MVO programs is a regularization technique that can mitigate the a…
Integrating prediction in mean-variance portfolio optimization
Andrew Butler, Roy H. Kwon
Prediction models are traditionally optimized independently from their use in the asset allocation decision-making process. We address this shortcoming and present a framework for…