International audienceWe show that the Laplace approximation of a supremum by Lp-norms has interesting consequences in optimization. For instance, the logarithmic barrier functions (LBF) of a primal convex problem P and its dual appear naturally when using this simple approximation technique for the value function g of P or its Legendre-Fenchel conjugate. In addition, minimizing the LBF of the dual is just evaluating the Cramer transform of the Laplace approximation of g. Finally, this technique permits to sometimes define an explicit dual problem in cases when the Legendre-Fenchel conjugate of g cannot be derived explicitly from its definition
This article concerns the recovery of the operators by noisy information in the case that their norm...
Algorithms for solving the nonlinear Lp (l 1 problem as the extreme case of the Lp problem. Numerica...
International audienceWe study the fundamental limits to the expressive power of neural networks. Gi...
In this paper, we formulate the lp-norm optimization problem as a conic optimization problem, derive...
norms In many optimization problems, a solution can be viewed as ascribing a “cost ” to each client ...
This paper examines a few relations between solution characteristics of an LP and the amount by whic...
In this paper, we formulate the l p -norm optimization problem as a conic optimization problem, deri...
In many optimization problems, a solution can be viewed as ascribing a “cost” to each client and the...
S for arbitrary set, K for convex cone, I g(·) is for arbitrary functions, not necessarily convex, I...
This paper suggests a method for obtaining efficiency bounds in models containing either only infini...
The lack of “closed form” solutions for the general linear models resulting from minimising the L0, ...
This study examines two different barrier functions and their use in both path-following and potenti...
Abstract. We study the problem of minimizing a sum of p-norms where p is a xed real number in the in...
Both supremum norms and 2-norms have found a huge number of applications as fitting and approximatio...
Besides the simplex algorithm, linear programs can also be solved via interior point methods. The th...
This article concerns the recovery of the operators by noisy information in the case that their norm...
Algorithms for solving the nonlinear Lp (l 1 problem as the extreme case of the Lp problem. Numerica...
International audienceWe study the fundamental limits to the expressive power of neural networks. Gi...
In this paper, we formulate the lp-norm optimization problem as a conic optimization problem, derive...
norms In many optimization problems, a solution can be viewed as ascribing a “cost ” to each client ...
This paper examines a few relations between solution characteristics of an LP and the amount by whic...
In this paper, we formulate the l p -norm optimization problem as a conic optimization problem, deri...
In many optimization problems, a solution can be viewed as ascribing a “cost” to each client and the...
S for arbitrary set, K for convex cone, I g(·) is for arbitrary functions, not necessarily convex, I...
This paper suggests a method for obtaining efficiency bounds in models containing either only infini...
The lack of “closed form” solutions for the general linear models resulting from minimising the L0, ...
This study examines two different barrier functions and their use in both path-following and potenti...
Abstract. We study the problem of minimizing a sum of p-norms where p is a xed real number in the in...
Both supremum norms and 2-norms have found a huge number of applications as fitting and approximatio...
Besides the simplex algorithm, linear programs can also be solved via interior point methods. The th...
This article concerns the recovery of the operators by noisy information in the case that their norm...
Algorithms for solving the nonlinear Lp (l 1 problem as the extreme case of the Lp problem. Numerica...
International audienceWe study the fundamental limits to the expressive power of neural networks. Gi...