☆ 4.7 Article

Learning Convex Optimization Models

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2021)

Least squares auto-tuning

Shane T. Barratt et al.

Summary: The article introduces a powerful proximal gradient method for least squares auto-tuning to find hyper-parameters in least squares problems. Numerical experiments using the MNIST dataset demonstrate the effectiveness of the method, cutting the test error of standard least squares in half.

ENGINEERING OPTIMIZATION (2021)

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Article Engineering, Multidisciplinary

Automatic repair of convex optimization problems

Shane Barratt et al.

Summary: This paper proposes a heuristic method for solving the smallest change that can make an infeasible, unbounded, or pathological convex optimization problem solvable, by minimizing a convex regularization function of the parameters subject to a solvability constraint. The method is based on the penalty method and utilizes efficient methods for evaluating derivatives of convex cone program solutions with respect to parameters.

OPTIMIZATION AND ENGINEERING (2021)

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Article Computer Science, Interdisciplinary Applications