A Flexible ADMM Algorithm for Big Data Applications

• Published in: Journal of scientific computing Vol. 71; no. 1; pp. 435 - 467
• Main Authors: Robinson, Daniel P., Tappenden, Rachael
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2017; Springer Nature B.V
• Subjects: Algorithms; Big Data; Computational Mathematics and Numerical Analysis; Convergence; Convex analysis; Lagrange multiplier; Linear programming; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematics; Mathematics and Statistics; Methods; Optimization; Regularization; Theoretical; Variables; 58C15; 68Q25; 65K05; Jacobi; 65Y20; Separable function; Gauss–Seidel; 90C30; Convex optimization; 65K10; 49M15; 49M37; 90C60; ADMM; Regularization

We present a Flexible Alternating Direction Method of Multipliers (F-ADMM) algorithm for solving optimization problems involving a strongly convex objective function that is separable into n ≥ 2 blocks, subject to (non-separable) linear equality constraints. The F-ADMM algorithm uses a Gauss–Seidel...