Abstract
Conjugation, or Legendre transformation, is a basic tool in convex analysis, rational mechanics, economics and optimization. It maps a function on a linear topological space into another one, defined in the dual of the linear space by coupling these spaces by means of the duality product. Generalized conjugation extends classical conjugation to any pair of domains, using an arbitrary coupling function between these spaces. This generalization of conjugation is now being widely used in optimal transportation problems, variational analysis and also optimization. If the coupled spaces are equal, generalized conjugations define order reversing maps of a family of functions into itself. In this case, it is natural to ask for the existence of fixed points of the conjugation, that is, functions which are equal to their (generalized) conjugated. Here we prove that any generalized symmetric conjugation has fixed points. The basic tool of the proof is a variational principle involving the order reversing feature of the conjugation. As an application of this abstract result, we will extend to real linear topological spaces a fixed-point theorem for Fitzpatrick's functions, previously proved in Banach spaces.
References
- V. I. Arnol′ d: Mathematical Methods of Classical Mechanics, 2nd Ed., Graduate Texts in Mathematics 60, Springer, New York (1989).https://doi.org/10.1007/978-1-4757-2063-1Google Scholar
- N. Bourbaki: Elements of Mathematics. Theory of Sets, Hermann, Paris (1968).Google Scholar
- R. S. Burachik, B. F. Svaiter: Maximal monotone operators, convex functions and a special family of enlargements, Technical Report A094, IMPA, Rio de Janeiro (2001); available at: http://www.preprint.impa.br/Shadows/SERIE_A/2001/94.html.Google Scholar
- R. S. Burachik, B. F. Svaiter: Maximal monotone operators, convex functions and a special family of enlargements, Set-Valued Anal. 10(4) (2002) 297–316.https://doi.org/10.1023/A:1020639314056Google Scholar
- S. Fitzpatrick: Representing monotone operators by convex functions, in: Functional Analysis and Optimization, Workshop / Miniconference (Canberra, 1988), Proc. Cent. Math. Anal. Aust. Natl. Univ. 20, Australian National University, Canberra (1988) 59–65.Google Scholar
- Xi-Nan Ma, N. S. Trudinger, Xu-Jia Wang: Regularity of potential functions of the optimal transportation problem, Arch. Ration. Mech. Anal. 177(2) (2005) 151–183.https://doi.org/10.1007/s00205-005-0362-9Google Scholar
- J.-E. Martinez-Legaz, M. Théra: A convex representation of maximal monotone operators, J. Nonlinear Convex Anal. 2(2) (2001) 243–247.Google Scholar
- J.-J. Moreau: Fonctionelles Convexes, Lecture Notes, College de France, Paris (1967).Google Scholar
- J.-J. Moreau: Inf-convolution, sous-additivité, convexité des fonctions numériques, J. Math. Pures Appl., IX. Sér. 49 (1970) 109–154.Google Scholar
- R. T. Rockafellar: Convex Analysis, Princeton Mathematical Series 28, Princeton University Press, Princeton (1970).Google Scholar
- R. T. Rockafellar, R. J.-B. Wets: Variational Analysis, Grundlehren der Mathematischen Wissenschaften 317, Springer, Berlin (1998).https://doi.org/10.1007/978-3-642-02431-3Google Scholar
- L. Rüschendorf: Optimal solutions of multivariate coupling problems, Appl. Math. 23(3) (1995) 325–338.https://doi.org/10.4064/am-23-3-325-338Google Scholar
- B. F. Svaiter: A family of enlargements of maximal monotone operators, Set-Valued Anal. 8(4) (2000) 311–328.https://doi.org/10.1023/A:1026555124541Google Scholar
- B. F. Svaiter: Fixed points in the family of convex representations of a maximal monotone operator, Proc. Amer. Math. Soc. 131(12) (2003) 3851–3859.https://doi.org/10.1090/S0002-9939-03-07083-7Google Scholar
- C. Villani: Topics in Optimal Transportation, Graduate Studies in Mathematics 58, American Mathematical Society, Providence (2003).https://doi.org/10.1090/gsm/058Google Scholar
- C. Villani: Optimal transport, old and new, Technical Report (2007).Google Scholar
Suggested citation
Published by Heldermann Verlag, 2011. Rights now held by Banach Press LLC.
