Measuring uncertainty without a norm.
Médianes vectorielles
Mehrdimensionale Hermite-Interpolation und numerische Integration.
Meilleure approximation en norme vectorielle et minima de Pareto
Meilleure approximation en norme vectorielle et théorie de la localisation
Meilleures approximations et médianes conditionnelles
Mémoire sur le calcul fonctionnel distributif
Mémoire sur les développements en fractions continues de la fonction exponentielle, pouvant servir d'introduction à la théorie des fractions continues algébriques
Meromorphe Approximationen
Meshless Polyharmonic Div-Curl Reconstruction
In this paper, we will discuss the meshless polyharmonic reconstruction of vector fields from scattered data, possibly, contaminated by noise. We give an explicit solution of the problem. After some theoretical framework, we discuss some numerical aspect arising in the problems related to the reconstruction of vector fields
Mesures d'irrationalité de log 2.
Méthodes stochastiques pour la détermination de polynômes de Bernstein
Methods for computing the least deviation from the sums of functions of one variable.
Metric projections and best approximants in Bochner-Orlicz spaces.
In the first section of this paper there are given criteria for strict convexity and smoothness of the Bochner-Orlicz space with the Orlicz norm as well as the Luxemburg norm. In the second one that geometrical properties are applied to the characterization of metric projections and zero mean valued best approximants to Bochner-Orlicz spaces.
Metric projections of closed subspaces of c₀ onto subspaces of finite codimension
Let X be a closed subspace of c₀. We show that the metric projection onto any proximinal subspace of finite codimension in X is Hausdorff metric continuous, which, in particular, implies that it is both lower and upper Hausdorff semicontinuous.
M-Ideals of Compact Operators.
Minimal multi-convex projections
We say that a function from is k-convex (for k ≤ L) if its kth derivative is nonnegative. Let P denote a projection from X onto V = Πₙ ⊂ X, where Πₙ denotes the space of algebraic polynomials of degree less than or equal to n. If we want P to leave invariant the cone of k-convex functions (k ≤ n), we find that such a demand is impossible to fulfill for nearly every k. Indeed, only for k = n-1 and k = n does such a projection exist. So let us consider instead a more general “shape” to preserve....
Minimal projections in tensor product spaces.
Minimal Projections in Tensor-Product Spaces.
Minimal projections onto subspaces of l∞(n) of codimension 2.