Open access paper about nonconvex features in SHOT

Date: March 20, 2021

The full paper about the nonconvex features in SHOT is now available online in the Journal of Global Optimization. The paper also includes some benchmarks on nonconvex MINLP and MIQCQP problems.

Lundell, A. and Kronqvist, J., Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT. Journal of Global Optimization (2021). https://doi.org/10.1007/s10898-021-01006-1​

SHOT available on the NEOS Server

Date: August 25, 2020

SHOT is now available as a solver on the NEOS Solver. You can submit jobs in GAMS format here.

The NEOS (Network-Enabled Optimization System) Server is a free internet-based service for solving numerical optimization problems. Visit the NEOS Server web site to access 60 state-of-the-art solvers in more than a dozen categories.

Call SHOT through GAMS' Pyomo and JuMP interfaces

Date: July 1, 2020

If you have a licensed version of GAMS (you can also request a free community license) it is also possible to call SHOT through GAMS' Pyomo and JuMP interfaces. Read more about it in this blog post or check out the Github repositories for GAMS.jl or the documentation page for the Pyomo interface.

Updated version of the SHOT paper

Date: June 10, 2020

An updated preprint of the SHOT paper:

Lundell, A., Kronqvist, J. and Westerlund T. The Supporting Hyperplane Optimization Toolkit for Convex MINLP. Optimization Online (2020). http://www.optimization-online.org/DB_FILE/2018/06/6680.pdf​

is now available.

SHOT 1.0 released

Date: May 28, 2020

SHOT 1.0 is now available at Github. Binaries are available for Windows, and Linux and MacOS users can easily compile SHOT themselves using the instructions on the page Compiling.

SHOT is also available in GAMS 31.1. A trial version can be downloaded from GAMS.

Preprint available about the nonconvex features in SHOT

Date: March 22, 2020

There is now a new preprint available that describes the nonconvex features in SHOT. The paper also includes some benchmarks on nonconvex MINLP and MIQCQP problems. It can be downloaded from Optimization Online:

Lundell, A. and Kronqvist, J., Polyhedral Approximation Strategies in Nonconvex Mixed-Integer Nonlinear Programming. Optimization Online (2020). http://www.optimization-online.org/DB_HTML/2020/03/7691.html​

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