WEBINAR / EVENT
Non-Convex Quadratic Optimization
This video shows one of the major new feature in Gurobi 9.0, the new bilinear solver, which allows users to solve problems with non-convex quadratic objectives and constraints such as QPs, QCPs, MIQPs, and MIQCPs.
September 01 2022
WEBINAR / EVENT
Non-Convex Quadratic Optimization
This video shows one of the major new feature in Gurobi 9.0, the new bilinear solver, which allows users to solve problems with non-convex quadratic objectives and constraints such as QPs, QCPs, MIQPs, and MIQCPs.
September 01 2022
WEBINAR / EVENT
Non-Convex Quadratic Optimization
This video shows one of the major new feature in Gurobi 9.0, the new bilinear solver, which allows users to solve problems with non-convex quadratic objectives and constraints such as QPs, QCPs, MIQPs, and MIQCPs.
September 01 2022
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.
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.
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.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Tobias Achterberg
Vice President of Research and Development
Dr. Achterberg studied mathematics and computer science at the Technical University of Berlin and the Zuse Institute Berlin. He finished his PhD in mathematics under supervision of Prof. Martin Grötschel in 2007. Dr. Achterberg is the author of SCIP, currently the best academic MIP solver. In addition to numerous publications in scientific journals, he has also received several awards for his dissertation and for SCIP, such as the Beale-Orchard-Hays Prize. From 2006, Dr. Achterberg worked for ILOG/IBM as developer of CPLEX in versions 11 to 12.6. Since 2014 he has been involved as a Senior Developer in the development of the Gurobi Optimizer. In his spare time, Dr. Achterberg likes to play the drums, read books, and play board games. Moreover, he enjoys going to rock and punk rock concerts.
Eli Towle
Senior Optimization Engineer
Dr. Eli Towle has a PhD in Industrial and Systems Engineering from the University of Wisconsin - Madison. His research focused on stochastic network interdiction problems with applications to nuclear weapons smuggling. He also explored theory for improving relaxations for a broad class of nonconvex optimization problems.
In his free time, Eli enjoys playing board games, Pathfinder RPG, and Magic: the Gathering.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Tobias Achterberg
Vice President of Research and Development
Dr. Achterberg studied mathematics and computer science at the Technical University of Berlin and the Zuse Institute Berlin. He finished his PhD in mathematics under supervision of Prof. Martin Grötschel in 2007. Dr. Achterberg is the author of SCIP, currently the best academic MIP solver. In addition to numerous publications in scientific journals, he has also received several awards for his dissertation and for SCIP, such as the Beale-Orchard-Hays Prize. From 2006, Dr. Achterberg worked for ILOG/IBM as developer of CPLEX in versions 11 to 12.6. Since 2014 he has been involved as a Senior Developer in the development of the Gurobi Optimizer. In his spare time, Dr. Achterberg likes to play the drums, read books, and play board games. Moreover, he enjoys going to rock and punk rock concerts.
Eli Towle
Senior Optimization Engineer
Dr. Eli Towle has a PhD in Industrial and Systems Engineering from the University of Wisconsin - Madison. His research focused on stochastic network interdiction problems with applications to nuclear weapons smuggling. He also explored theory for improving relaxations for a broad class of nonconvex optimization problems.
In his free time, Eli enjoys playing board games, Pathfinder RPG, and Magic: the Gathering.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Vice President of Research and Development
Tobias Achterberg
Dr. Achterberg studied mathematics and computer science at the Technical University of Berlin and the Zuse Institute Berlin. He finished his PhD in mathematics under supervision of Prof. Martin Grötschel in 2007. Dr. Achterberg is the author of SCIP, currently the best academic MIP solver. In addition to numerous publications in scientific journals, he has also received several awards for his dissertation and for SCIP, such as the Beale-Orchard-Hays Prize. From 2006, Dr. Achterberg worked for ILOG/IBM as developer of CPLEX in versions 11 to 12.6. Since 2014 he has been involved as a Senior Developer in the development of the Gurobi Optimizer. In his spare time, Dr. Achterberg likes to play the drums, read books, and play board games. Moreover, he enjoys going to rock and punk rock concerts.
Senior Optimization Engineer
Eli Towle
Dr. Eli Towle has a PhD in Industrial and Systems Engineering from the University of Wisconsin - Madison. His research focused on stochastic network interdiction problems with applications to nuclear weapons smuggling. He also explored theory for improving relaxations for a broad class of nonconvex optimization problems.
In his free time, Eli enjoys playing board games, Pathfinder RPG, and Magic: the Gathering.
