Imperial College London


Faculty of EngineeringDepartment of Computing

Visiting Researcher



j.kronqvist Website CV




302Huxley BuildingSouth Kensington Campus





I was working as a postdoc at Imperial College (Royal Society- Newton International Fellow) from 2019 - 2021, and my research is mainly within the field of mixed integer nonlinear programming (MINLP). Currently I am working as Assistant professor in Optimization and Systems Theory at KTH Royal Institute of Technology in Sweden, and I have ongoing collaboration projects with the Computational Optimisation Group (COG) at Imperial.   

Graduated 2018 with honors from Åbo Akademi University in Finland, and I was given an award for best PhD thesis at the Faculty of Science and Engineering (see thesis). I was awarded a Newton International Fellowship by the Royal Society in 2018. I was also awarded a grant by the Foundations Post Doc Pool (given by the Swedish Cultural Foundation in Finland) to support my postdoc research.  

During my time as a PhD student, I have also been working as a university teacher at Åbo Akademi Univeristy in 2014-2015 and 2017-2019. During these years, I have been lecturing and in charge of six different courses in process systems engineering.   

Research interests

  • Optimization (integer programming)
  • Operations research 
  • Process systems engineering
  • Machine learning

Developer of the SHOT solver for convex MINLP

SHOT, or the Supporting Hyperplane Optimization Toolkit, is an open source solver for convex MINLP problems. The solver iteratively constructs a polyhedral outer approximation of the problem, and obtains an equivalent linear MILP problem. Extensive benchmarks have shown that SHOT is among the most efficient solvers for convex MINLP, e.g., see the following review paper.  The solver is open source and can be downloaded from

SHOT was awarded the COIN-OR Cup in 2018 for best contribution to the COIN-OR initiative, which is an open source initiative within Operations Research (see announcement).



Kronqvist J, Misener R, 2020, A disjunctive cut strengthening technique for convex MINLP, Optimization and Engineering, Vol:22, ISSN:1389-4420, Pages:1315-1345

Kronqvist J, Bernal DE, Grossmann IE, 2020, Using regularization and second order information in outer approximation for convex MINLP, Mathematical Programming, Vol:180, ISSN:0025-5610, Pages:285-310


Tsay C, Kronqvist J, Thebelt A, et al., 2021, Partition-based formulations for mixed-integer optimization of trained ReLU neural networks, arXiv

Kronqvist J, Misener R, Tsay C, 2021, Between steps: Intermediate relaxations between big-M and convex hull formulations, arXiv

More Publications