Book “Lezioni di Ricerca Operativa” (in Italian): . [1] M. Fischetti, ” Worst-Case Analysis of an Approximation Scheme for the Subset-Sum Problem”, . Teacher in charge, MATTEO FISCHETTI ยท [email protected], MAT/09 Matteo Fischetti, Lezioni di Ricerca Operativa. Padova: Progetto, Cerca nel . Main course: Ricerca operativa e Ottimizzazione / Operation Research And Optimization Matteo Fischetti, Lezioni di Ricerca Operativa. Progetto Libreria.

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Top Italian Scientists in Mathematics: Book “Lezioni di Ricerca Operativa” in Italian: Plenary speaker at MatheuristicAngra dos Reis, September Chair of the international Program Committee for the meeting “Integer Programming lezikni Combinatorial Optimization”, First International Prize “Best Ph. Revolution” jointly with the Netherlands Railways team: Winner of the Harold Larnder Prize awarded annually to an individual who has achieved international distinction in Operational Operativ, Methods and Studies B.


Maffioli, “k -Cardinality Trees: Guida, “I Turni delle Locomotive” in S.

Fischetti, “Crew Scheduling for Netherlands Railways: Destination Customer”, in S. Salazar, “Partial Cell Suppression: Monaci, “How tight is the corner relaxation?

Ybema, “The New Dutch Timetable: Luzzi, “Mixed-integer programming models for nesting problems”, Journal of Heuristics 15 3, Zanette, “Fast approaches to improve the robustness of a railway timetable”, Transportation Science 43, Monaci, “Light Robustness”, in R. Salvagnin, “Feasibility Pump 2.

Salvagnin, “Just MIP it! Zanette, “On the enumerative nature of Gomory’s dual cutting plane method”, Mathematical Fischeetti B, Tramontani, “On the separation of disjunctive cuts”, Mathematical Programming A, Balas, “Lexicography and degeneracy: Zanette, “A hard integer program made easy by lexicography”, Mathematical Programming Opeativa1, Fischetti, “A Lagrangian heuristic for robustness, with an application to train timetabling”, Transportation Science 46 1, Monaci, “Exploiting erraticism in search”, Operations Research 62 1, Sinnl, “Intersection cuts for bilevel optimization”, in Integer Programming and Combinatorial Optimization: Sinnl, “Benders decomposition without separability: Salvagnin, “On handling indicator constraints in mixed integer programming”, Computational Optimization and Applications65, Sinnl, “Thinning out Steiner trees: Monaci, “Using a general-purpose MILP solver for the practical solution of real-time train rescheduling”, European Journal of Operational Research1, Sinnl, “A new general-purpose algorithm for mixed-integer bilevel linear programs”, Operations Research 65 6, Jo, “Deep neural networks and mixed integer linear optimization”, Constraints 23 3, Ruthmair, “Least cost influence propagation in social networks”, Mathematical Programming, Automated optimized array design for functional near-infrared spectroscopy”, Neurophotonics 5 3Jul-Sepdoi: Sinnl, “On the use of intersection cuts for bilevel optimization”, Mathematical Programming 1,doi: Recent technical reports some available at www.


Salvagnin, “Faster SGD training by minibatch persistency”,