Daniele Catanzaro

Scientific Production

This webpage reports on my scientific production and is intended for my own use. In particular, the works listed below are classified by application areas and concern exclusively manuscripts submitted to or published in international peer-review scientific journals. Some of the linked files may be covered by publisher copyrights. If you reached this page, please ensure that your institution has the appropriate subscription before accessing any of the PDFs below.

Keywords: combinatorial optimization, integer programming, polyhedral combinatorics, decomposition algorithms, computational complexity, routing, network design, coloring, covering, partitioning, location, games on graphs, constructive characterizations, high performance computing, massive parallel enumeration, enumeration of trees, lattices of unrooted binary trees, tree imbalance, tree coding, Huffman coding, entropy encoding, cross-entropy encoding, Kullback–Leibler divergence, tree metric, submodular functions, convexity, Schur convexity, phylogenetics, phylogenetic networks, mathematical modeling of tumor progression, genome-wide association studies, medical bioinformatics, deep reinforcement learning, clustering, optimization for machine learning.

Scopus author output analysis 

Network design with applications on mathematics of evolution and computational phylogenetics

Coloring and covering with applications on haplotyping & disease association studies

Graph theory

  • D. Catanzaro, S. Chaplick, S. Felsner, B. V. Halldórsson, M. M. Halldórsson, T. Hixon, and J. Stacho. Max point-tolerance graphs. Discrete Applied Mathematics, 216(1): 84-97, 2017.  

Cutting plane methods

Telecommunications, manufacturing, scheduling, and routing under uncertainty

Articles submitted to international peer-review journals:

  • D. Catanzaro, R. Pesenti, R. Ronco. A new fast and accurate solution approach for the automatic scene detection problem. Computers and Operations Research, minor revision pending, June 2021.
  • H. Dehaybe, D. Catanzaro, and P. Chevalier. A Deep Reinforcement Learning approach for the Stochastic Inventory Problem. Operations Research, February 2021.
  • D. Catanzaro, M. Frohn, O. Gascuel, and R. Pesenti. Twenty Years of Balanced Minimum Evolution. European Journal of Operational Research, December 2020. Invited article, minor revision pending.

Other Publications (Book Chapters, Conference Proceedings…)

Feel free to contact me in the case instances of specific problems or source codes for any of the above works may be needed.