Adresse postale:

IA - LGIPM, University of Lorraine,
3 rue Augustin Fresnel, BP 45112,
57073 Metz Cedex 03, France
Tel: +33 3 72 74 79 51 
Fax: +33 3 72 74 79 56
Email: hoai-an.le-thi@univ-lorraine dot fr


   Le Thi Hoai An
    Professeur des Universités Classe Exceptionnelle
    Directrice du Département Informatique & Applications, LGIPM
    Université de Lorraine.








  • Ancienne Élève de l'Ecole Normale Supérieure de Hanoi, Viet Nam (1976-1980).
  • Doctorat: Université de Rouen, France (Octobre 1992 - Décembre 1994).
  • Habilitation à Diriger des Recherches: Université de Rouen, France (Juin 1997).





  1982 - 1991: Enseignante-chercheuse  à l'ENS de Hanoi.


  1994 - 1997: Chercheuse à l'INSA-Rouen.


  1998 - 2003: Maître de Conférences à l'INSA-Rouen.


  2003 - 2008: Professeur 2e classe à l'université de Paul Verlaine - Metz.


  2008 - 2012: Professeur 1e classe à l'université de Paul Verlaine - Metz.


  Depuis 2012: Professeur Classe Exceptionnelle à l'université de Lorraine. 


Le Thi Hoai An
 earned her PhD with Highest Distinction in Optimization in 1994, and her Habilitation in 1997 both from university of Rouen, France. From 1998 to 2003 she was Associate Professor at the National Institute for Applied Sciences, Rouen. Since 2003 she is Full Professor at the University of Paul Verlaine – Metz (which becomes University of Lorraine in 2012). She is currently serving as Full Professor exceptional class, University of Lorraine. She is the author/co-author of more than 230 journal articles, international conference papers and book chapters, the co-editor of 7 books and 12 special issues of international journals, and supervisor of 30 PhD theses. She is Chair of Scientific Committee and Chair of Organizing Committee as well as member of Scientific Committee of numerous International Conferences, and leader of several regional/national/international projects. Her research interests include Numerical Analysis, Optimization and Operations Research and their applications in Information Systems and Industrial Complex Systems in various fields such as, Data Mining and Machine Learning, Telecommunication, Transportation, Supply Chain and Management, Finance, Bioinformatics, Image Analysis, Cryptology, Security & Reliability. She is the co-founder (with Pham Dinh Tao) of DC programming and DCA, an innovative approach in nonconvex programming and global optimization. These theoretical and algorithmic tools, becoming now classic and increasingly popular, have been successfully applied by researchers and practitioners all the world over to model and solve their real-world smooth/nonsmooth nonconvex programs, especially in the large-scale setting.






           6th International Conference on Computer Science, Applied Mathematics and Applications


           ICCSAMA 2019, December 19-20, 2019, Hanoi, Vietnam





      6th World Congress on Global Optimization WCGO 2019, July 8-10, 2019, Metz, France




            Howard Rosenbrock Prize 2017


This prize is awarded annually to honor the authors of the best paper published in Optimization and Engineering journal in the previous year. 

The winners of the 2017 Rosenbrock Prize are Hoai An Le Thi and Tao Pham Dinh for their work titled, Difference of convex functions algorithms (DCA) for image restoration via a Markov random field model.



30th birthday of DC programming and DCA, 2015







  • New subgradient extragradient methods for solving monotone bilevel equilibrium problems. Optimization, Volume 68, Issue 11, pp. 2097-2122, 16 September 2019
  • Smoothing techniques and difference of convex functions algorithms for image reconstructions. Optimization, pp. 1-33, 2019. Published online: 03 August 2019
  • Group variable selection via lp,0 regularization and application to optimal scoring. Neural Networks, Volume 118, pp. 220-234, October 2019
  • DC programming and DCA for Supply Chain and Production Management: state-of-the-art models and methods. International Journal of Production Research. Published online: 29 August 2019
  • Improved DC programming approaches for solving the quadratic eigenvalue complementarity problem. Applied Mathematics and Computation, Volume 353, pp. 95-113, July 2019 
  • A unified DC programming framework and efficient DCA based approaches for large scale batch reinforcement learning. Journal of Global Optimization, Volume 73, Issue 2, pp. 279-310, February 2019
  • Convergence Analysis of Difference-of-Convex Algorithm with Subanalytic Data. Journal of Optimization Theory and Applications, Volume 179, Issue 1, pp. 103–126, October 2018
  • Accelerated Difference of Convex functions Algorithm and its Application to Sparse Binary Logistic Regression. IJCAI 2018, Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 1369-1375, 2018
  • DC programming and DCA: thirty years of developments. Mathematical Programming, Volume 169, Issue 1, pp. 5-68, May 2018


      De plus ...