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A sum-of-squares optimization program is a … 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. statistics, finance and machine learning.
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https://github.com/JuliaOpt/SumOfSquares.jl +
, http://homepages.laas.fr/henrion/software/gloptipoly/ +
, http://www.cds.caltech.edu/sostools/ +
, https://github.com/wangjie212/TSSOS +
, https://github.com/oxfordcontrol/CDCS +
, https://github.com/JuliaOpt/JuMP.jl +
, https://github.com/oxfordcontrol/CDCS/tree/master/packages/%2Bcdcs_sos +
, https://github.com/peterwittek/ncpol2sdpa +
, https://github.com/lanl-ansi/MomentOpt.jl +
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| rdfs:comment |
A sum-of-squares optimization program is a … 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.f relaxations in semidefinite programming.
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| rdfs:label |
Sum-of-squares optimization
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