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On this page, news about and related to SHOT are presented.
Paper about SHOT published in Journal of Global Optimization
Date: February 10, 2022
The final version of the SHOT paper has been made available online. It is available as open access.
Lundell, A., Kronqvist, J. and Westerlund T. The Supporting Hyperplane Optimization Toolkit for Convex MINLP. Journal of Global Optimization (2022). https://link.springer.com/article/10.1007/s10898-022-01128-0
SHOT 1.1 released
Major new features
- SHOT no longer needs an external NLP solver in its primal strategy since SHOT can now call itself for solving fixed NLP problems. Activated with
Primal.FixedInteger.Solver=2. For nonconvex problems it is still recommended to use an external NLP solver. - Partition convex nonseparable quadratic functions as separate constraints using an eigenvalue decomposition-based reformulation. Activated with
Model.Reformulation.Quadratics.UseEigenValueDecomposition=true. - Support for performing an initial polyhedral approximation of the nonlinear feasible set before feasibility-based bound tightening. Activated with
Model.BoundTightening.InitialPOA.Use=true. - Support for problems containing semi-continuous and semi-integer variables.
- Support for problems containing special ordered sets.
Minor improvements and bug fixes
- Improved support for generating supporting hyperplanes for the entire nonlinear feasible set instead of the feasible sets for the individual constraint functions. Activated with Dual.ESH.Rootsearch.UseMaxFunction=true.
- Improved support for passing nonconvex quadratic functions directly to the MIP solver (if supported).
- Bug fixes for the AMPL interface.
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
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
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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Contents
Paper about SHOT published in Journal of Global Optimization
SHOT 1.1 released
Major new features
Minor improvements and bug fixes
Open access paper about nonconvex features in SHOT
SHOT available on the NEOS Server
Call SHOT through GAMS' Pyomo and JuMP interfaces
Updated version of the SHOT paper
SHOT 1.0 released
Preprint available about the nonconvex features in SHOT