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303. J. William Helton, Igor Klep, Scott McCullough:
The matricial relaxation of a linear matrix inequality.

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Submission: 2010, March 3.

Given linear matrix inequalities (LMIs) L_1 and L_2, it is natural to ask: (Q1) when does one dominate the other, that is, does L_1(X) PsD imply L_2(X) PsD? (Q2) when do they have the same solution set? Such questions can be NP-hard. This paper describes a natural relaxation of an LMI, based on substituting matrices for the variables x_j. With this relaxation, the domination questions (Q1) and (Q2) have elegant answers, indeed reduce to constructible semidefinite programs. Assume there is an X such that L_1(X) and L_2(X) are both PD, and suppose the positivity domain of L_1 is bounded. For our "matrix variable" relaxation a positive answer to (Q1) is equivalent to the existence of matrices V_j such that L_2(x)=V_1^* L_1(x) V_1 + ... + V_k^* L_1(x) V_k. As for (Q2) we show that, up to redundancy, L_1 and L_2 are unitarily equivalent.
Such algebraic certificates are typically called Positivstellensštze and the above are examples of such for linear polynomials. The paper goes on to derive a cleaner and more powerful Putinar-type Positivstellensatz for polynomials positive on a bounded set of the form {X | L(X) PsD}.
An observation at the core of the paper is that the relaxed LMI domination problem is equivalent to a classical problem. Namely, the problem of determining if a linear map from a subspace of matrices to a matrix algebra is "completely positive".

Mathematics Subject Classification (2000): 46L07, 14P10, 90C22, 11E25, 46L89, 13J30.

Keywords and Phrases: linear matrix inequality (LMI), completely positive, semidefinite programming, Positivstellensatz, Gleichstellensatz, archimedean quadratic module, free positivity.

Full text, 34p.: dvi 225k, ps.gz 435k, pdf 533k.

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