Results 11 to 20 of about 549 (53)
Improving the linear relaxation of maximum k-cut with semidefinite-based constraints
EURO Journal on Computational Optimization, 2019 We consider the maximum k-cut problem that involves partitioning the vertex set of a graph into k subsets such that the sum of the weights of the edges joining vertices in different subsets is maximized.
VilmarJefté Rodrigues de Sousa +2 more
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Upper bounds for packings of spheres of several radii
Forum of Mathematics, Sigma, 2014 We give theorems that can be used to upper bound the densities of packings of different spherical caps in the unit sphere and of translates of different convex bodies in Euclidean space. These theorems extend the linear programming bounds for packings of
DAVID DE LAAT +2 more
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THE GROTHENDIECK CONSTANT IS STRICTLY SMALLER THAN KRIVINE’S BOUND
Forum of Mathematics, Pi, 2013 The (real) Grothendieck constant ${K}_{G} $ is the infimum over those $K\in (0, \infty )$ such that for every $m, n\in \mathbb{N} $ and every $m\times n$ real matrix $({a}_{ij} )$ we have $$\begin{eqnarray*}\displaystyle \max _{\{ x_{i}\} _{i= 1}^{m} , \{
MARK BRAVERMAN +3 more
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Convergence of a short‐step primal‐dual algorithm based on the Gauss‐Newton direction
Journal of Applied Mathematics, Volume 2003, Issue 10, Page 517-534, 2003., 2003 We prove the theoretical convergence of a short‐step, approximate path‐following, interior‐point primal‐dual algorithm for semidefinite programs based on the Gauss‐Newton direction obtained from minimizing the norm of the perturbed optimality conditions. This is the first proof of convergence for the Gauss‐Newton direction in this context.
Serge Kruk, Henry Wolkowicz
wiley +1 more source
On global optimization with indefinite quadratics
EURO Journal on Computational Optimization, 2017 We present an algorithmic framework for global optimization problems in which the non-convexity is manifested as an indefinite-quadratic as part of the objective function.
Marcia Fampa, Jon Lee, Wendel Melo
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A note on a matrix version of the Farkas lemma [PDF]
, 2012 A linear polyomial non-negative on the non-negativity domain of finitely many linear polynomials can be expressed as their non-negative linear combination.
Zalar, Aljaž
core +1 more source
Adaptive First-Order Methods for General Sparse Inverse Covariance Selection [PDF]
, 2009 In this paper, we consider estimating sparse inverse covariance of a Gaussian graphical model whose conditional independence is assumed to be partially known.
Lu, Zhaosong
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Correction Bounds on measures satisfying moment conditions
, 2004 The Annals of Applied Probability (2002) 12 1114 ...
Lasserre, Jean B.
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Determinantal representations of semi-hyperbolic polynomials [PDF]
, 2016 We prove a generalization of the Hermitian version of the Helton-Vinnikov determinantal representation of hyperbolic polynomials to the class of semi-hyperbolic polynomials, a strictly larger class, as shown by an example.
Knese, Greg
core +3 more sources
Forum of Mathematics, SigmaA spectral convex set is a collection of symmetric matrices whose range of eigenvalues forms a symmetric convex set. Spectral convex sets generalize the Schur-Horn orbitopes studied by Sanyal–Sottile–Sturmfels (2011). We study this class of convex bodies,
Raman Sanyal, James Saunderson
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