Results 11 to 20 of about 585 (68)
EURO Journal on Computational Optimization, 2021 A conic optimization problem is a problem involving a constraint that the optimization variable be in some closed convex cone. Prominent examples are linear programs (LP), second order cone programs (SOCP), semidefinite problems (SDP), and copositive ...
Mirjam Dür, Franz Rendl
doaj
Certificates of convexity for basic semi-algebraic sets [PDF]
, 2010 We provide two certificates of convexity for arbitrary basic semi-algebraic sets of $\R^n$. The first one is based on a necessary and sufficient condition whereas the second one is based on a sufficient (but simpler) condition only. Both certificates are
Lasserre, Jean B.
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, 2017 Recently, M. Bouafoa, et al. [5] (Journal of optimization Theory and Applications, August, 2016),investigated a new kernel function which differs from the self-regular kernel functions. The kernel function has a trigonometric Barrier Term.
M. E. Ghami
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Error bound for a perturbed minimization problem related with the sum of smallest eigenvalues
, 2010 Let C be a n×n symmetric matrix. For each integer 1 < k < n we consider the minimization problem m(e): = minX{ Tr{CX} + eƒ(X)}. Here the variable X is an n×n symmetric matrix, whose eigenvalues satisfy the number e is a positive (perturbation) parameter ...
M. V. Travaglia
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PREDICTOR–CORRECTOR SMOOTHING NEWTON METHOD FOR SOLVING SEMIDEFINITE PROGRAMMING
, 2009 There has been much interest recently in smoothing methods for solving semidefinite programming (SDP). In this paper, based on the equivalent transformation for the optimality conditions of SDP, we present a predictor–corrector smoothing Newton algorithm
Caiying Wu, Guoqing Chen
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On semidefinite representations of non-closed sets [PDF]
, 2009 Spectrahedra are sets defined by linear matrix inequalities. Projections of spectrahedra are called semidefinitely representable sets. Both kinds of sets are of practical use in polynomial optimization, since they occur as feasible sets in semidefinite ...
Netzer, Tim
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Polynomial Optimization with Real Varieties [PDF]
, 2013 We consider the optimization problem of minimizing a polynomial f(x) subject to polynomial constraints h(x)=0, g(x)>=0. Lasserre's hierarchy is a sequence of sum of squares relaxations for finding the global minimum. Let K be the feasible set.
Nie, Jiawang
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Hyperbolicity cones of elementary symmetric polynomials are spectrahedral [PDF]
, 2013 We prove that the hyperbolicity cones of elementary symmetric polynomials are spectrahedral, i.e., they are slices of the cone of positive semidefinite matrices.
Brändén, Petter
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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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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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