|
:''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. Sum-of-squares optimization techniques have been successfully applied by researchers in the control engineering field.〔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.〕〔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.〕〔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, 73–85.〕 ==Optimization problem== The problem can be expressed as : subject to : Here "SOS" represents the class of SOS polynomials. The vector and polynomials are given as part of the data for the optimization problem. The quantities are the decision variables. SOS programs can be converted to semidefinite programs (SDPs) using the connection between SOS polynomials and positive-semidefinite matrices. 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Sum-of-squares optimization」の詳細全文を読む スポンサード リンク
|