807 research outputs found

    Measures with zeros in the inverse of their moment matrix

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    We investigate and discuss when the inverse of a multivariate truncated moment matrix of a measure Ό\mu has zeros in some prescribed entries. We describe precisely which pattern of these zeroes corresponds to independence, namely, the measure having a product structure. A more refined finding is that the key factor forcing a zero entry in this inverse matrix is a certain conditional triangularity property of the orthogonal polynomials associated with Ό\mu.Comment: Published in at http://dx.doi.org/10.1214/07-AOP365 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Semidefinite approximations of projections and polynomial images of semialgebraic sets

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    Given a compact semialgebraic set S of R^n and a polynomial map f from R^n to R^m, we consider the problem of approximating the image set F = f(S) in R^m. This includes in particular the projection of S on R^m for n greater than m. Assuming that F is included in a set B which is simple (e.g. a box or a ball), we provide two methods to compute certified outer approximations of F. Method 1 exploits the fact that F can be defined with an existential quantifier, while Method 2 computes approximations of the support of image measures.The two methods output a sequence of superlevel sets defined with a single polynomial that yield explicit outer approximations of F. Finding the coefficients of this polynomial boils down to computing an optimal solution of a convex semidefinite program. We provide guarantees of strong convergence to F in L^1 norm on B, when the degree of the polynomial approximation tends to infinity. Several examples of applications are provided, together with numerical experiments

    A prolongation-projection algorithm for computing the finite real variety of an ideal

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    We provide a real algebraic symbolic-numeric algorithm for computing the real variety VR(I)V_R(I) of an ideal II, assuming it is finite while VC(I)V_C(I) may not be. Our approach uses sets of linear functionals on R[X]R[X], vanishing on a given set of polynomials generating II and their prolongations up to a given degree, as well as on polynomials of the real radical ideal of II, obtained from the kernel of a suitably defined moment matrix assumed to be positive semidefinite and of maximum rank. We formulate a condition on the dimensions of projections of these sets of linear functionals, which serves as stopping criterion for our algorithm. This algorithm, based on standard numerical linear algebra routines and semidefinite optimization, combines techniques from previous work of the authors together with an existing algorithm for the complex variety. This results in a unified methodology for the real and complex cases.Comment: revised versio

    New approximations for the cone of copositive matrices and its dual

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    We provide convergent hierarchies for the cone C of copositive matrices and its dual, the cone of completely positive matrices. In both cases the corresponding hierarchy consists of nested spectrahedra and provide outer (resp. inner) approximations for C (resp. for its dual), thus complementing previous inner (resp. outer) approximations for C (for the dual). In particular, both inner and outer approximations have a very simple interpretation. Finally, extension to K-copositivity and K-complete positivity for a closed convex cone K, is straightforward.Comment: 8

    Exploiting symmetries in SDP-relaxations for polynomial optimization

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    In this paper we study various approaches for exploiting symmetries in polynomial optimization problems within the framework of semi definite programming relaxations. Our special focus is on constrained problems especially when the symmetric group is acting on the variables. In particular, we investigate the concept of block decomposition within the framework of constrained polynomial optimization problems, show how the degree principle for the symmetric group can be computationally exploited and also propose some methods to efficiently compute in the geometric quotient.Comment: (v3) Minor revision. To appear in Math. of Operations Researc

    On representations of the feasible set in convex optimization

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    We consider the convex optimization problem min⁥{f(x):gj(x)≀0,j=1,...,m}\min \{f(x) : g_j(x)\leq 0, j=1,...,m\} where ff is convex, the feasible set K is convex and Slater's condition holds, but the functions gjg_j are not necessarily convex. We show that for any representation of K that satisfies a mild nondegeneracy assumption, every minimizer is a Karush-Kuhn-Tucker (KKT) point and conversely every KKT point is a minimizer. That is, the KKT optimality conditions are necessary and sufficient as in convex programming where one assumes that the gjg_j are convex. So in convex optimization, and as far as one is concerned with KKT points, what really matters is the geometry of K and not so much its representation.Comment: to appear in Optimization Letter

    Reionization Constraints on the Contribution of Primordial Compact Objects to Dark Matter

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    Many lines of evidence suggest that nonbaryonic dark matter constitutes roughly 30% of the critical closure density, but the composition of this dark matter is unknown. One class of candidates for the dark matter is compact objects formed in the early universe, with typical masses M between 0.1 and 1 solar masses to correspond to the mass scale of objects found with microlensing observing projects. Specific candidates of this type include black holes formed at the epoch of the QCD phase transition, quark stars, and boson stars. Here we show that accretion onto these objects produces substantial ionization in the early universe, with an optical depth to Thomson scattering out to z=1100 of approximately tau=2-4 [f_CO\epsilon_{-1}(M/Msun)]^{1/2} (H_0/65)^{-1}, where \epsilon_{-1} is the accretion efficiency \epsilon\equiv L/{\dot M}c^2 divided by 0.1 and f_CO is the fraction of matter in the compact objects. The current upper limit to the scattering optical depth, based on the anisotropy of the microwave background, is approximately 0.4. Therefore, if accretion onto these objects is relatively efficient, they cannot be the main component of nonbaryonic dark matter.Comment: 12 pages including one figure, uses aaspp4, submitted to Ap
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