2002
DOI: 10.1007/bf03167449
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Positive semidefinite relaxations for distance geometry problems

Abstract: We consider distance geometry problems for determining r-dimensional coordinates of n points from a given set of the distances between the points. This problem is a fundamental problem in molecular biology for finding the structure of proteins from NMR (Nuclear Magnetic Resonance) data.We formulate the problem as the minimization of an error function defined by the sum of the absolute differences, which is a typical nonconvex optimization problem. We show that this problem can be reduced into a concave quadrat… Show more

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Cited by 4 publications
(4 citation statements)
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“…A remark about Yajima's relaxation: although it was introduced specifically for the i DGP, it was originally solved using an ad-hoc interior point method. Even though our results show it underperforms on average with respect to Mosek, this does not negate the (good) results reported in [56].…”
Section: Weeding Out Obvious Loserscontrasting
confidence: 83%
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“…A remark about Yajima's relaxation: although it was introduced specifically for the i DGP, it was originally solved using an ad-hoc interior point method. Even though our results show it underperforms on average with respect to Mosek, this does not negate the (good) results reported in [56].…”
Section: Weeding Out Obvious Loserscontrasting
confidence: 83%
“…This formulation was proposed in [56]. The term 2 {u,v}∈E X uv added to the objective function is equal to Tr(1X) (where 1 is the all-one matrix) and has a regularization purpose, ensuring that Tr(1X) = 0 and hence that rk(X) ≤ n − 1.…”
Section: Yajima's Sdp Relaxationmentioning
confidence: 99%
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“…The local methods only find a single EDM completion. These algorithms are based mainly on semidefinite programming, a subfield of convex optimization [3,104,219]. In this approach, given a partial symmetric matrix A with nonnegative elements and zero diagonal, one of its feasible EDM completions, the matrix D, is computed by solving the convex optimization problem minimize H…”
Section: The Euclidean Distance Matrix Completion Problemmentioning
confidence: 99%