Geometric & Functional Analysis GAFA

, Volume 5, Issue 6, pp 853–923 | Cite as

Sobolev-Gagliardo-Nirenberg and Markov type inequalities on subanalytic domains

  • L. P. Bos
  • P. D. Milman


In this work we introduce a new parameter,s≥1, in the well known Sobolev-Gagliardo-Nirenberg (abbreviated SGN) inequalities and show their validity (with an appropriates) for any compact subanalytic domain. The classical form of these SGN inequalities (s=1 in our formulation) fails for domains with outward pointing cusps. Our parameters measures the degree of cuspidality of the domain. For regular domainss=1. We also introduce an extension, depending on a parameter σ≥1, to several variables of a local form of the classical Markov inequality on the derivatives of a polynomial in terms of its own values, and show the equivalence of Markov and SGN inequalities with the same value of parameters, σ=s. Our extension of Markov's inequality admits, in the case of supremum norms, a geometric characterization. We also establish several other characterizations: the existence of a bounded (linear) extension ofC functions with a homogeneous loss of differentiability, and the validity of a global Markov inequality. Our methods may broadly be classified as follows:
  1. 1.

    Desingularization and anL p -version of Glaeser-type estimates. In fact we obtain a bounds<-2d+1, whered is the maximal order of vanishing of the jacobian of the desingularization map of the domain.

  2. 2.

    Interpolation type inequalities for norms of functions and Bernstein-Markov type inequalities for multivariate polynomials (classical analysis).

  3. 3.

    Geometric criteria for the validity of local Markov inequalities (local analysis of the singularities of domains).

  4. 4.

    Multivariate Approximation Theory.


Thus our approach brings together the calculus of Glaeser-type estimates from differential analysis, the algebra of desingularization, the geometry of Markov type inequalities and the analysis of Sobolev-Nirenberg type estimates. Our exposition takes into account this interdisciplinary nature of the methods we exploit and is almost entirely self-contained. /lt>


Type Inequality Local Form Maximal Order Type Estimate Supremum Norm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Verlag 1995

Authors and Affiliations

  • L. P. Bos
    • 1
  • P. D. Milman
    • 2
  1. 1.Department of MathematicsUniversity of CalgaryCalgaryCanada
  2. 2.Department of MathematicsUniversity of TorontoTorontoCanada

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