© 2003

Convex Variational Problems

Linear, Nearly Linear and Anisotropic Growth Conditions

  • Authors

Part of the Lecture Notes in Mathematics book series (LNM, volume 1818)

Table of contents

  1. Front Matter
    Pages N2-X
  2. Michael Bildhauer
    Pages 1-12
  3. Michael Bildhauer
    Pages 173-183
  4. Michael Bildhauer
    Pages 185-198
  5. Michael Bildhauer
    Pages 205-206
  6. Michael Bildhauer
    Pages 207-213
  7. Michael Bildhauer
    Pages 215-217
  8. Back Matter
    Pages 219-219

About this book


The author emphasizes a non-uniform ellipticity condition as the main approach to regularity theory for solutions of convex variational problems with different types of non-standard growth conditions.

This volume first focuses on elliptic variational problems with linear growth conditions. Here the notion of a "solution" is not obvious and the point of view has to be changed several times in order to get some deeper insight. Then the smoothness properties of solutions to convex anisotropic variational problems with superlinear growth are studied. In spite of the fundamental differences, a non-uniform ellipticity condition serves as the main tool towards a unified view of the regularity theory for both kinds of problems.


Non-standard growth Smooth function anisotropic growth linear growth minimizers regularity

Bibliographic information

  • Book Title Convex Variational Problems
  • Book Subtitle Linear, Nearly Linear and Anisotropic Growth Conditions
  • Authors Michael Bildhauer
  • Series Title Lecture Notes in Mathematics
  • Series Abbreviated Title LNM
  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Softcover ISBN 978-3-540-40298-5
  • eBook ISBN 978-3-540-44885-3
  • Series ISSN 0075-8434
  • Series E-ISSN 1617-9692
  • Edition Number 1
  • Number of Pages XII, 220
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Calculus of Variations and Optimal Control; Optimization
    Partial Differential Equations
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