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Computational Optimization and Applications

, Volume 68, Issue 3, pp 473–478 | Cite as

COAP 2016 Best Paper prize

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Each year, the editorial board of Computational Optimization and Applications (COAP) selects a paper from the preceding year’s publications for the Best Paper Award. In 2016, 97 papers were published by COAP. The recipients of the 2016 Best Paper Award are Pietro Belotti (FICO Xpress), Pierre Bonami (IBM-CPLEX), Matteo Fischetti (University of Padova), Andrea Lodi (École Polytechnique de Montréal), Michele Monaci (University of Bologna), Amaya Nogales-Gómez (Huawei Paris), and Domenico Salvagnin (University of Padova) for their paper “On handling indicator constraints in mixed integer programming” published in volume 65, pages 545–566. This article highlights the research related to the award winning paper.

The paper [2] addresses possible ways to handle disjunctive constraints, i.e., constraints that either hold or are relaxed depending on the value of a binary variable. Typically, these conditions are modelled by using indicator constraints, i.e., by associating a valid large...

References

  1. 1.
    Balas, E.: Disjunctive programming. Ann. Discrete Math. 5, 3–51 (1979)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Belotti, P., Bonami, P., Fischetti, M., Lodi, A., Monaci, M., Nogales-Gómez, A., Salvagnin, D.: On handling indicator constraints in mixed integer programming. Comput. Optim. Appl. 65(3), 545–566 (2016)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Bertsimas, D.: Statistics and machine learning via a modern optimization lens. INFORMS, The 2014–2015 Philip McCord Morse Lecture, pp. 1–42 (2014)Google Scholar
  4. 4.
    Bonami, P., Lodi, A., Tramontani, A., Wiese, S.: On mathematical programming with indicator constraints. Math. Program. 151(1), 191–223 (2015)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Brooks, J.: Support vector machines with the ramp loss and the hard margin loss. Oper. Res. 59(2), 467–479 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Ceria, S., Soares, J.: Convex programming for disjunctive convex optimization. Math. Program. 86(3), 595–614 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Grossmann, I.E., Trespalacios, F.: Systematic modeling of discrete-continuous optimization models through generalized disjunctive programming. AIChE J. 59(9), 3276–3295 (2013)CrossRefGoogle Scholar
  8. 8.
    Shen, X., Tseng, G., Zhang, X., Wong, W.: On \(\psi \)-learning. J. Am. Stat. Assoc. 98(463), 724–734 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B., Yu, P., Zhou, Z.-H., Steinbach, M., Hand, D., Steinberg, D., Steinberg, D.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2007)CrossRefGoogle Scholar

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