Exploiting sparsity in the coefficient matching conditions in sum-of-squares programming using ADMM

Zheng Y, Fantuzzi G, Papachristodoulou A (2017)

Publication Type: Journal article

Publication year: 2017

Journal

IEEE Control Systems Letters Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 1

Pages Range: 80-85

Journal Issue: 1

DOI: 10.1109/LCSYS.2017.2706941

Abstract

This letter introduces an efficient first-order method based on the alternating direction method of multipliers (ADMM) to solve semidefinite programs arising from sum-of-squares (SOS) programming. We exploit the sparsity of the coefficient matching conditions when SOS programs are formulated in the usual monomial basis to reduce the computational cost of the ADMM algorithm. Each iteration of our algorithm requires one projection onto the positive semidefinite cone and the solution of multiple quadratic programs with closed-form solutions free of any matrix inversion. Our techniques are implemented in the open-source MATLAB solver SOSADMM. Numerical experiments on SOS problems arising from unconstrained polynomial minimization and from Lyapunov stability analysis for polynomial systems show speed-ups compared to the interior-point solver SeDuMi, and the first-order solver CDCS.

Authors with CRIS profile

Giovanni Fantuzzi

Involved external institutions

University of Oxford GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Zheng, Y., Fantuzzi, G., & Papachristodoulou, A. (2017). Exploiting sparsity in the coefficient matching conditions in sum-of-squares programming using ADMM. IEEE Control Systems Letters, 1(1), 80-85. https://dx.doi.org/10.1109/LCSYS.2017.2706941

MLA:

Zheng, Yang, Giovanni Fantuzzi, and Antonis Papachristodoulou. "Exploiting sparsity in the coefficient matching conditions in sum-of-squares programming using ADMM." IEEE Control Systems Letters 1.1 (2017): 80-85.

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