the date of receipt and acceptance should be inserted later Abstract Tensor-based methods are receiving a growing interest in scientific comput-ing for the numerical solution of problems defined in high dimensional tensor product spaces. A family of methods called Proper Generalized Decompositions methods have been recently introduced for the a priori construction of tensor approximations of the solution of such problems. In this paper, we give a mathematical analysis of a family of progressive and updated Proper Generalized Decompositions for a particular class of problems associated with the minimization of a convex functional over a reflexive tensor Banach space
International audienceModel reduction techniques based on the construction of separated representati...
© 2016 Society for Industrial and Applied Mathematics. This paper studies models and algorithms for ...
We study a new class of structured Schatten norms for tensors that includes two recently proposed no...
International audienceTensor-based methods are receiving a growing interest in scientific computing ...
AbstractThe Proper Generalized Decomposition (PGD) is a methodology initially proposed for the solut...
Agraïments: A. Nouy supported by GdR MoMaS with partners ANDRA, BRGM, CEA, CNRS, EDF, IRSNThe Proper...
In this paper, we analyse the Basic Tensor Methods, which use approximate solutions of the auxiliary...
Nonlinear optimization problems in complex variables are frequently encountered in applied mathemati...
In the current version we present a translation into English of the main derivations, which first ap...
AbstractIn this paper, we first give a simple proof of the decomposition theorem in Alber (Field Ins...
In this paper we develop new tensor methods for unconstrained convex optimization, which solve at ea...
International audienceThe Proper Generalized Decomposition (PGD) is a methodology initially proposed...
In this paper, we study the auxiliary problems that appear in p-order tensor methods for unconstrain...
In this paper, we study local convergence of high-order Tensor Methods for solving convex optimizati...
We propose a general non-accelerated tensor method under inexact information on higher- order deriva...
International audienceModel reduction techniques based on the construction of separated representati...
© 2016 Society for Industrial and Applied Mathematics. This paper studies models and algorithms for ...
We study a new class of structured Schatten norms for tensors that includes two recently proposed no...
International audienceTensor-based methods are receiving a growing interest in scientific computing ...
AbstractThe Proper Generalized Decomposition (PGD) is a methodology initially proposed for the solut...
Agraïments: A. Nouy supported by GdR MoMaS with partners ANDRA, BRGM, CEA, CNRS, EDF, IRSNThe Proper...
In this paper, we analyse the Basic Tensor Methods, which use approximate solutions of the auxiliary...
Nonlinear optimization problems in complex variables are frequently encountered in applied mathemati...
In the current version we present a translation into English of the main derivations, which first ap...
AbstractIn this paper, we first give a simple proof of the decomposition theorem in Alber (Field Ins...
In this paper we develop new tensor methods for unconstrained convex optimization, which solve at ea...
International audienceThe Proper Generalized Decomposition (PGD) is a methodology initially proposed...
In this paper, we study the auxiliary problems that appear in p-order tensor methods for unconstrain...
In this paper, we study local convergence of high-order Tensor Methods for solving convex optimizati...
We propose a general non-accelerated tensor method under inexact information on higher- order deriva...
International audienceModel reduction techniques based on the construction of separated representati...
© 2016 Society for Industrial and Applied Mathematics. This paper studies models and algorithms for ...
We study a new class of structured Schatten norms for tensors that includes two recently proposed no...