Sum-of-squares optimization

This article deals with sum-of-squares constraints. For problems with sum-of-squares cost functions, see Least squares.

A sum-of-squares optimization program is an optimization problem with a linear cost function and a particular type of constraint on the decision variables. These constraints are of the form that when the decision variables are used as coefficients in certain polynomials, those polynomials should have the polynomial SOS property. When fixing the maximum degree of the polynomials involved, sum-of-squares optimization is also known as the Lasserre hierarchy of relaxations in semidefinite programming.

Sum-of-squares optimization techniques have been applied across a variety of areas, including control theory (in particular, for searching for polynomial Lyapunov functions for dynamical systems described by polynomial vector fields), statistics, finance and machine learning.[1][2][3][4]

  1. ^ Sum of squares : theory and applications : AMS short course, sum of squares : theory and applications, January 14-15, 2019, Baltimore, Maryland. Parrilo, Pablo A.; Thomas, Rekha R. Providence, Rhode Island: American Mathematical Society. 2020. ISBN 978-1-4704-5025-0. OCLC 1157604983.{{cite book}}: CS1 maint: others (link)
  2. ^ Tan, W., Packard, A., 2004. "Searching for control Lyapunov functions using sums of squares programming". In: Allerton Conf. on Comm., Control and Computing. pp. 210–219.
  3. ^ Tan, W., Topcu, U., Seiler, P., Balas, G., Packard, A., 2008. Simulation-aided reachability and local gain analysis for nonlinear dynamical systems. In: Proc. of the IEEE Conference on Decision and Control. pp. 4097–4102.
  4. ^ A. Chakraborty, P. Seiler, and G. Balas, "Susceptibility of F/A-18 Flight Controllers to the Falling-Leaf Mode: Nonlinear Analysis," AIAA Journal of Guidance, Control, and Dynamics, vol. 34 no. 1 (2011), pp. 73–85.