We consider the class of smooth convex functions defined over an open convex set. We show that this class is essentially different than the class of smooth convex functions defined over the entire linear space by exhibiting a function that belongs to the former class but cannot be extended to the entire linear space while keeping its properties. We proceed by deriving new properties of the class under consideration, including an inequality that is strictly stronger than the classical Descent Lemma.

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  1. D. Azagra, C. Mudarra: An extension theorem for convex functions of class C 1,1 on Hilbert spaces, J. Math. Anal. Appl. 446(2) (2017) 1167–1182.https://doi.org/10.1016/j.jmaa.2016.09.015Google Scholar
  2. H. Bauschke, P. L. Combettes: Convex Analysis and Monotone Operator Theory in Hilbert Spaces, Springer, Berlin (2011).https://doi.org/10.1007/978-1-4419-9467-7Google Scholar
  3. H. H. Bauschke, Y. Lucet, H. M. Phan: On the convexity of piecewise-defined functions, ESAIM: Control Optim. Calc. Variations 22(3) (2016) 728–742.https://doi.org/10.1051/cocv/2015023Google Scholar
  4. A. Beck: First-Order Methods in Optimization, Siam, Philadelphia (2017).https://doi.org/10.1137/1.9781611974997Google Scholar
  5. A. Daniilidis, M. Haddou, E. Le Gruyer, O. Ley: Explicit formulas for C 1,1 Glaeser-Whitney extensions of 1-Taylor fields in Hilbert spaces, Proc. Amer. Math. Soc. 146(10) (2018) 4487–4495.https://doi.org/10.1090/proc/14012Google Scholar
  6. E. de Klerk, F. Glineur, A. Taylor: Worst-case convergence analysis of inexact gradient and Newton methods through semidefinite programming performance estimation, SIAM J. Optimization 30(3) (2020) 2053–2082.https://doi.org/10.1137/19M1281368Google Scholar
  7. Y. Drori: The exact information-based complexity of smooth convex minimization, J. Complexity 39 (2017) 1–16.https://doi.org/10.1016/j.jco.2016.11.001Google Scholar
  8. A. S. Nemirovsky, D. B. Yudin: Problem Complexity and Method Efficiency in Optimization, John Wiley & Sons, New York (1983).Google Scholar
  9. Y. Nesterov: Introductory Lectures on Convex Optimization: A Basic Course, Applied Optimization 87, Springer, New York (2004).https://doi.org/10.1007/978-1-4419-8853-9Google Scholar
  10. Y. Nesterov, A. Nemirovskii: Interior-point Polynomial Algorithms in Convex Programming, SIAM Studies in Applied and Numerical Mathematics 13, SIAM, Philadelphia (1994).https://doi.org/10.1137/1.9781611970791Google Scholar
  11. J. M. Ortega, W. C. Rheinboldt: Iterative Solution of Nonlinear Equations in Several Variables, Classics in Applied Mathematics 30, SIAM, Philadelphia (1970).Google Scholar
  12. B. T. Polyak: Introduction to Optimization, Translations Series in Mathematics and Engineering, Optimization Software Publications, New York (1987).Google Scholar
  13. R. T. Rockafellar: Convex Analysis, Princeton University Press, Princeton (2015).Google Scholar
  14. A. B. Taylor, J. M. Hendrickx, F. Glineur: Smooth strongly convex interpolation and exact worst-case performance of first-order methods, Math. Programming 161(1-2) (2017) 307–345.https://doi.org/10.1007/s10107-016-1009-3Google Scholar
  15. H. Whitney: Analytic extensions of differentiable functions defined in closed sets, Trans. Amer. Math. Soc. 36(1) (1934) 63–89.https://doi.org/10.1090/S0002-9947-1934-1501735-3Google Scholar

Y. Drori. “On the Properties of Convex Functions over Open Sets.” Journal of Convex Analysis 27 (2020), No. 4, 1303–1314. https://doi.org/10.68381/jca27069