Means and generalized means.
Mean-value theorem for vector-valued functions
For a differentiable function where is a real interval and , a counterpart of the Lagrange mean-value theorem is presented. Necessary and sufficient conditions for the existence of a mean such that are given. Similar considerations for a theorem accompanying the Lagrange mean-value theorem are presented.
Measurability of multifunctions of two variables [Book]
Measurability of real functions defined on the product of metric spaces
Measure concentration for compound Poisson distributions.
Measure theoretic zero sets in infinite dimensional spaces and differentiability of Lipschitz mappings
Measure-preserving quality within mappings.
In [6], Guy David introduced some methods for finding controlled behavior in Lipschitz mappings with substantial images (in terms of measure). Under suitable conditions, David produces subsets on which the given mapping is bilipschitz, with uniform bounds for the bilipschitz constant and the size of the subset. This has applications for boundedness of singular integral operators and uniform rectifiability of sets, as in [6], [7], [11], [13]. Some special cases of David's results, concerning projections...
Measuring asymptotic convexity
Meda inequality for rearrangements of the convolution on the Heisenberg group and some applications.
Mémoire sur les fonctions discontinues
Mémoire sur les intégrales définies, prises entre des limites imaginaires
Mémoire sur l'intégration d'une classe de fonctions transcendantes.
Meromorphic functions whose Julia sets contain a free Jordan arc.
Mesure de Haar en analyse non-standard
Mesure de Mahler et calcul de constantes universelles pour les polynomes de N variables.
Méthodes de transformation fondées sur la conservation d'une relation invariable entre les dérivées de même ordre.
Metric diophantine approximation in Julia sets of expanding rational maps
Metric space valued functions of bounded variation
Metrically convex functions in normed spaces
Properties of metrically convex functions in normed spaces (of any dimension) are considered. The main result, Theorem 4.2, gives necessary and sufficient conditions for a function to be metrically convex, expressed in terms of the classical convexity theory.
Metrics on vector spaces with -localisation.