2011
DOI: 10.1007/s10107-011-0505-8
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The tracial moment problem and trace-optimization of polynomials

Abstract: Abstract. The main topic addressed in this paper is trace-optimization of polynomials in noncommuting (nc) variables: given an nc polynomial f , what is the smallest trace f (A) can attain for a tuple of matrices A ? A relaxation using semidefinite programming (SDP) based on sums of hermitian squares and commutators is proposed. While this relaxation is not always exact, it gives effectively computable bounds on the optima. To test for exactness, the solution of the dual SDP is investigated. If it satisfies a … Show more

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Cited by 34 publications
(37 citation statements)
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“…This fact does not generalize to higher powers or more variables (see examples in [KS08a,KS08b,Qua15]). In [BK12,BCKP13] the authors obtain the tracial analogs of the results on the classical moment problem of Curto and Fialkow, Stochel, Bayer and Teichmann, Fialkow and Nie [FN10], providing powerful means to tackle the special cases of the TTMP in a way analogous way to the classical one.…”
mentioning
confidence: 99%
“…This fact does not generalize to higher powers or more variables (see examples in [KS08a,KS08b,Qua15]). In [BK12,BCKP13] the authors obtain the tracial analogs of the results on the classical moment problem of Curto and Fialkow, Stochel, Bayer and Teichmann, Fialkow and Nie [FN10], providing powerful means to tackle the special cases of the TTMP in a way analogous way to the classical one.…”
mentioning
confidence: 99%
“…. , X n ))/d (so that the identity matrix has trace one) [7,6,8,21]. The moment approach for these problems relies on minimizing L(f ), where L is a linear functional on the space of noncommutative polynomials satisfying some necessary conditions, and L(f ) models ψ * f (X 1 , .…”
Section: Techniques From Noncommutative Polynomial Optimizationmentioning
confidence: 99%
“…Similarly we can use semidefinite programming to test whether a given nc polynomial f ∈ R X is an element of Θ 2 as first observed in [19], see also [10,7,6]. The method behind it is a variant of the Gram matrix method:…”
Section: Sums Of Hermitian Squares (With Commutators) and Semidefinitmentioning
confidence: 99%