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
AbstractWe introduce two diagonal preconditioners, one of them is a scaling of the matrix. They are ...
International audienceDifference-of-Convex programming and related algorithms, which constitute the ...
AbstractIn this paper we establish sufficient convergence conditions for the (MSOR) method when the ...
AbstractLet φ and ψ be any norms on Rm and Rn respectively. We study a subgradient method for comput...
SIGLECNRS-CDST / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
Abstract We study convergence properties of Dikin's affine scaling algorithm applied to nonconv...
We study the subgradient projection method for convex optimization with Brannlund 's level cont...
This paper proposes an algorithm for solving structured optimization problems, which covers both the...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
AbstractLet U denote the set of n × n unitary matrices equipped with the Euclidean topology. Let H b...
This paper describes a new technique to find the minimum norm solution of a linear program. The main...
Minimizing a simple nonsmooth outer function composed with a smooth inner map offers a versatile fra...
We analyze the rate of local convergence of the augmented Lagrangian method in nonlinear semidefinit...
Elsner L, He C, Mehrmann V. Minimization of the norm, the norm of the inverse and the condition numb...
AbstractWe introduce two diagonal preconditioners, one of them is a scaling of the matrix. They are ...
International audienceDifference-of-Convex programming and related algorithms, which constitute the ...
AbstractIn this paper we establish sufficient convergence conditions for the (MSOR) method when the ...
AbstractLet φ and ψ be any norms on Rm and Rn respectively. We study a subgradient method for comput...
SIGLECNRS-CDST / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
Abstract We study convergence properties of Dikin's affine scaling algorithm applied to nonconv...
We study the subgradient projection method for convex optimization with Brannlund 's level cont...
This paper proposes an algorithm for solving structured optimization problems, which covers both the...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
We consider the NP-hard problem of finding a minimum norm vector in n-dimensional real or complex Eu...
AbstractLet U denote the set of n × n unitary matrices equipped with the Euclidean topology. Let H b...
This paper describes a new technique to find the minimum norm solution of a linear program. The main...
Minimizing a simple nonsmooth outer function composed with a smooth inner map offers a versatile fra...
We analyze the rate of local convergence of the augmented Lagrangian method in nonlinear semidefinit...
Elsner L, He C, Mehrmann V. Minimization of the norm, the norm of the inverse and the condition numb...
AbstractWe introduce two diagonal preconditioners, one of them is a scaling of the matrix. They are ...
International audienceDifference-of-Convex programming and related algorithms, which constitute the ...
AbstractIn this paper we establish sufficient convergence conditions for the (MSOR) method when the ...
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