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Event Recap

Ill conditioning in LP and MILP models remains a challenge for optimization practitioners. The condition number of a square matrix provides a measure of the level of ill conditioning, but it offers limited insight into causes or remedies. Several LP and MILP solvers offer functionality that provides explanations of infeasibility, but until now, none have offered similar functionality to provide concise explanations of ill conditioning.

 

In this hands-on webinar, Dr. Ed Klotz describes gurobi-modelanalyzer, an open-source repository containing an ill conditioning explainer that filters out rows or columns of an ill conditioned basis matrix. The resulting submatrix often facilitates a diagnosis of the source of ill conditioning that would be significantly more difficult if examining the entire basis matrix. While the underlying computational methodology will be discussed, the tutorial will focus on how to interpret the explanations, using examples based on publicly available models.

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Dr. Ed Klotz

Senior Mathematical Optimization Specialist

Dr. Ed Klotz

Senior Mathematical Optimization Specialist

Dr. Ed Klotz has nearly 30 years of experience in the mathematical optimization software industry. He is a technical expert who, over the course of his career, has worked with a wide array of customers to help them solve some of world’s most challenging mathematical optimization problems. In his role as a Senior Mathematical Optimization Specialist on the Gurobi R&D team, Dr. Klotz works closely with our customers to support them in implementing and utilizing mathematical optimization in their organizations.

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