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Ed Klotz
Ed Klotz

Ed Klotz

Senior Mathematical Optimization Specialist
Gurobi Optimization
Bio

Dr. Ed Klotz has over 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. He also interacts heavily with the R&D team based on his experiences with the customers.

Prior to joining Gurobi, Dr. Klotz was a member of the CPLEX development team of IBM. He was involved in product development, customer training, product documentation, and numerous other tasks, with a primary focus on delivering CPLEX customer support and leveraging his experiences with customers to help inform the R&D team about customer needs and product improvements. Dr. Klotz has extensive knowledge in linear programming, integer programming, and numerical linear algebra for finite precision computing. Using this knowledge, he was able to investigate customer support issues at the source code level and identify potential improvements in CPLEX, both in terms of performance and accuracy of computation.

Before joining IBM, Dr. Klotz was a principal technical support engineer at ILOG, Inc., and a mathematical programming specialist at CPLEX Optimization, Inc.

Dr. Klotz has presented at numerous conferences, workshops, and web seminars and published numerous papers on mathematical optimization. His interests are in all aspects of mathematical programming, with a primary interest in research that can impact mathematical programming software. He obtained a BA in Math and Economics from Oberlin College and a PhD in Operations Research from Stanford University.


Quantum Computers for Operations Research Practitioners

Quantum computers have received extensive publicity during the last two decades.  They have shown potential to outperform classical computers for challenging computational tasks, including problems of interest in optimization and data analysis. However, significant questions remain regarding effectively translating the underlying theory into practical, scalable machines.  The resulting debate between quantum enthusiasts and quantum skeptics often presents conflicting viewpoints that can be difficult to assess for those with little familiarity with the underlying concepts. Much of the discussion either assumes familiarity with the language of quantum computing or only describes the concepts at a high level, posing challenges for a technical audience with backgrounds outside the realm of quantum physics.

This presentation will not take sides in the debate or make predictions about future quantum computing capabilities. Instead, it will focus on the fundamental technical ideas underlying quantum computers in a way that will resonate with those familiar with mathematical optimization or data science.  While quantum computing is often associated with unfamiliar notation and concepts, many of its core ideas are naturally expressed using linear algebra. Others closely relate to reformulation techniques familiar from integer and quadratic optimization. Seen from this perspective, a background in optimization can significantly lower the barrier to understanding how a quantum computing environment operates.

By developing this foundational understanding, the audience will be better equipped to evaluate current claims, assess the relevance of quantum approaches to their own optimization problems, and engage more productively with subsequent presentations and panel discussions at this conference.

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