Non-Convex Quadratic Optimization

Webinar Summary

One major new feature in Gurobi 9.0 is a new bilinear solver, which allows users to solve problems with non-convex quadratic objectives and constraints (i.e., QPs, QCPs, MIQPs, and MIQCPs). Many non-linear optimization solvers search for locally optimal solutions to these problems.

In contrast, Gurobi can now solve these problems to global optimality. Non-convex quadratic optimization problems arise in various industrial applications. In particular, non-convex quadratic constraints are vital to solve classical pooling and blending problems.

In this webinar session, we will:

  • Introduce MIQCPs and mixed-integer bilinear programming
  • Discuss algorithmic ideas for handling bilinear constraints
  • Show a live demo of how Gurobi 9.0 supports bilinear constraints by building and solving a small instance of the pooling problem

 

Presented Materials

You can download the PDF with the slides here and the pooling problem Jupyter Notebook here.

Meet the Experts

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search

Gurobi Optimization