Every year, Optimization and Engineering (OPTE) honors excellence in scientific research by presenting the Rosenbrock Prize to the best paper we published the previous year. The prize recognizes outstanding research contributions that demonstrate Howard Rosenbrock’s own dedication to bridging the gap between optimization and engineering. I am delighted to announce this year’s winners of the 2021 Rosenbrock Prize are Jan Kronqvist and Ruth Misener for their paper, A disjunctive cut strengthening technique for convex MINLP (Kronqvist and Misener 2022). Jan Kronqvist is assistant professor at KTH Royal Institute of Technology, and Ruth Misener is professor at Imperial College London. The authors will receive a $500 prize sponsored by Springer.

This year’s winning paper was chosen by a committee consisting of committee-chair Michel Gamache (Polytechnique Montreal, Canada) and committee-members Frauke Liers (Friedrich-Alexander-Universitaet, Germany) and Arvind Raghunathan (Mitsubishi Electric Research Laboratories, USA). The committee produced the following citation for the selected paper:

The paper presents a new framework for strengthening cutting planes of nonlinear convex constraints to obtain tighter outer approximations for solving convex mixed-integer nonlinear programming (MINLP) problems. This paper focuses on deriving strong cutting planes by exploiting disjunctive structures in the problem.

The members of the selection committee particularly appreciated the well-structured theoretical approach that supports the development of cutting planes of nonlinear convex constraints. The paper is well written, and clear algorithms, figures and tests (carried out on convex MINLP instances from MINLPLib - MINLPLib 2020) make this a complete and very interesting article. The article is relevant from a theoretical point of view since it addresses a class of problem strongly studied in the OR scientific community, but also relevant for its practical aspect given that MINLP optimization arises in many applications across engineering, manufacturing, and the natural sciences. More precisely, convex MINLP is highly relevant in process synthesis, portfolio optimization, and constrained layout.

Previous winners of the award were

  1. 1.

    Jason E. Hicken (Hicken 2014),

  2. 2.

    Moritz Simon and Michael Ulbrich (Simon and Ulbrich 2015),

  3. 3.

    Taedong Kim and Stephen J. Wright (Kim and Wright 2016),

  4. 4.

    Hoai An Le Thi and Tao Pham Dinh (Thi and Dinh 2017),

  5. 5.

    Laurent Hoeltgen, Michael Breuß, Gert Herold, and Ennes Sarrad (Hoeltgen et al. 2018),

  6. 6.

    Robert Burlacu, Herbert Egger, Martin Groß, Alexander Martin, Marc E. Pfetsch, Lars Schewe, Mathias Sirvent, and Martin Skutella (Burlacu et al. 2019), and

  7. 7.

    Martine Labbé, Fränk Plein, and Martin Schmidt (Labbé et al. 2020).

OPTE is an intellectual forum for engineers and optimization researchers. We feature cutting-edge optimization research as well as challenging and successful optimization implementations in engineering. We invite researchers to submit high-quality papers discussing algorithms, applications, and theory at the intersection of optimization and engineering.