AbstractLet φ and ψ be any norms on Rm and Rn respectively. We study a subgradient method for computing the associated bound norm Sφψ(A) = sup{φ(Ax), ψ(x)⩽1} (a nonconvex optimization problem). It is proved that homodual method converges when one of the norms φ and ψ is polyhedral
The problem of maximizing the p-th power of a p-norm over a halfspace-presented polytope in Rd is a ...
L'optimisation des modèles non convexes en haute dimension a toujours été un problème difficile et f...
The convergence of numerical approximations to the solutions of differential equations is a key aspe...
AbstractLet φ and ψ be any norms on Rm and Rn respectively. We study a subgradient method for comput...
L'approximation des convexes lisses par des polytopes pour la distance de Hausdorff a connu de nombr...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
RésuméLe problème inverse des valeurs propres est la recherche d'une matrice diagonale X, telle que ...
AbstractLet f : Rn → (−∞, ∞] be a convex polyhedral function. We show how to find the normal minimiz...
This book aims to give an introduction to generalized derivative concepts useful in deriving necessa...
We study the convergence rate of a hierarchy of upper bounds for polynomial optimization problems, p...
AbstractA survey of results concerning vectorial norms is presented, for fixed point problems in sev...
140 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1983.In this thesis techniques for...
Die Arbeit „A New Relaxation Technique for Polynomial Optimization and Spectrahedral Geometry Proble...
L’optimisation est le domaine des mathématiques appliquées qui s’intéresse à la minimi-sation (ou la...
Global convergence theorems for a class of descent methods for unconstrained optimization problems i...
The problem of maximizing the p-th power of a p-norm over a halfspace-presented polytope in Rd is a ...
L'optimisation des modèles non convexes en haute dimension a toujours été un problème difficile et f...
The convergence of numerical approximations to the solutions of differential equations is a key aspe...
AbstractLet φ and ψ be any norms on Rm and Rn respectively. We study a subgradient method for comput...
L'approximation des convexes lisses par des polytopes pour la distance de Hausdorff a connu de nombr...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
RésuméLe problème inverse des valeurs propres est la recherche d'une matrice diagonale X, telle que ...
AbstractLet f : Rn → (−∞, ∞] be a convex polyhedral function. We show how to find the normal minimiz...
This book aims to give an introduction to generalized derivative concepts useful in deriving necessa...
We study the convergence rate of a hierarchy of upper bounds for polynomial optimization problems, p...
AbstractA survey of results concerning vectorial norms is presented, for fixed point problems in sev...
140 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1983.In this thesis techniques for...
Die Arbeit „A New Relaxation Technique for Polynomial Optimization and Spectrahedral Geometry Proble...
L’optimisation est le domaine des mathématiques appliquées qui s’intéresse à la minimi-sation (ou la...
Global convergence theorems for a class of descent methods for unconstrained optimization problems i...
The problem of maximizing the p-th power of a p-norm over a halfspace-presented polytope in Rd is a ...
L'optimisation des modèles non convexes en haute dimension a toujours été un problème difficile et f...
The convergence of numerical approximations to the solutions of differential equations is a key aspe...
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