WEBINAR / EVENT
Products of Variables in Mixed Integer Programming
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints. As a consequence, there are many mixed integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
September 01 2022
WEBINAR / EVENT
Products of Variables in Mixed Integer Programming
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints. As a consequence, there are many mixed integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
September 01 2022
WEBINAR / EVENT
Products of Variables in Mixed Integer Programming
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints. As a consequence, there are many mixed integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
September 01 2022
Webinar Summary
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization, and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints.
As a consequence, there are many mixed-integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
In this webinar, we will:
Explain how such product relationships can be detected in a given mixed integer linear program
Demonstrate ideas on how they can be exploited to improve the performance of an MILP solver
Describe cuts from the Reformulation Linearization Technique (RLT) and cuts for the Boolean Quadric Polytope (BQP)
Present preliminary computational results from these techniques in Gurobi version 9.0
Presented Materials
You can download the materials associated with this webinar here.
Webinar Summary
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization, and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints.
As a consequence, there are many mixed-integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
In this webinar, we will:
Explain how such product relationships can be detected in a given mixed integer linear program
Demonstrate ideas on how they can be exploited to improve the performance of an MILP solver
Describe cuts from the Reformulation Linearization Technique (RLT) and cuts for the Boolean Quadric Polytope (BQP)
Present preliminary computational results from these techniques in Gurobi version 9.0
Presented Materials
You can download the materials associated with this webinar here.
Webinar Summary
Products of problem variables appear naturally in quadratic programs. Special preprocessing, linearization, and cutting plane techniques are available to deal with such products. If at least one of the two variables in a product is binary, then the product can be modeled using a set of linear constraints.
As a consequence, there are many mixed-integer linear programs (MILPs) that actually contain products of variables hidden in their constraint structure. Rediscovering these product relationships between the variables enables us to exploit the solving techniques for product terms.
In this webinar, we will:
Explain how such product relationships can be detected in a given mixed integer linear program
Demonstrate ideas on how they can be exploited to improve the performance of an MILP solver
Describe cuts from the Reformulation Linearization Technique (RLT) and cuts for the Boolean Quadric Polytope (BQP)
Present preliminary computational results from these techniques in Gurobi version 9.0
Presented Materials
You can download the materials associated with this webinar 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.
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.
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.
