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Duality for Discrete Optimization: Theory and Applications Ted Ralphs1 Joint work with Suresh Bolusani1, Scott DeNegre3, Metal Gzelsoy2, Anita Hassanzadeh4, Sahara Tahernejad1 21 CORAL Labs, Department
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How to fill out duality for discrete optimization
How to fill out duality for discrete optimization
01
Define the primal problem for discrete optimization.
02
Formulate the Lagrangian by adding the dual variables multiplied by the constraints to the objective function.
03
Find the dual function by minimizing the Lagrangian with respect to the primal variables.
04
Derive the dual problem by maximizing the dual function subject to the constraints being the dual variables non-negative.
05
Solve the dual problem to find the optimal dual variables.
06
Use the optimal dual variables to obtain the optimal value of the primal problem.
Who needs duality for discrete optimization?
01
Researchers and practitioners in the field of discrete optimization who are looking to efficiently solve combinatorial problems.
02
Individuals working on scheduling, routing, allocation, and other optimization problems with discrete decision variables.
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What is duality for discrete optimization?
Duality for discrete optimization refers to the relationship between a primal optimization problem and its corresponding dual problem.
Who is required to file duality for discrete optimization?
Researchers, practitioners, or any individual working on discrete optimization problems may need to consider duality for optimal solutions.
How to fill out duality for discrete optimization?
Duality for discrete optimization is typically addressed through mathematical formulations and solution methods.
What is the purpose of duality for discrete optimization?
The purpose of duality in this context is to provide insights into the optimal solutions of a given optimization problem.
What information must be reported on duality for discrete optimization?
Information related to the primal problem, dual problem, and the relationship between them should be included in duality analysis for discrete optimization.
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