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Webinar Summary

In this webinar, you will discover how to exploit parallelism in linear programming and mixed-integer programming.

As core counts grow in modern computers, it is becoming more and more important to exploit parallelism wherever you can find it. This presentation will take a broad look at parallelism in mathematical optimization. We’ll look at parallelism in continuous optimization, discussing why opportunities can often be quite limited. We’ll also look at parallelism in MIP, where opportunities are larger but significant challenges still arise. Finally, we’ll talk about alternative parallel architectures, including distributed computing and GPUs, and whether they are likely to provide performance improvement across a broad set of practical problems in the future.

In this webinar, you will learn:

  • How to exploit parallelism in mathematical optimization.
  • How distributed computing and GPUs can provide performance improvement across a broad set of practical problems.

Presented Materials

You can download the slides presented in this webinar here.

Meet the Experts

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Dr. Edward Rothberg

Chief Scientist and Chairman of the Board
Gurobi Optimization

Dr. Edward Rothberg

Chief Scientist and Chairman of the Board
Gurobi Optimization

Dr. Rothberg has served in senior leadership positions in optimization software companies for more than twenty years. Prior to his role as Gurobi CEO, Dr. Rothberg held the Gurobi COO position since co-founding Gurobi in 2008, and prior to that he led the ILOG CPLEX team. Dr. Edward Rothberg has a BS in Mathematical and Computational Science from Stanford University, and an MS and PhD in Computer Science, also from Stanford University. Dr. Rothberg has published numerous papers in the fields of linear algebra, parallel computing, and mathematical programming. He is one of the world's leading experts in sparse Cholesky factorization and computational linear, integer, and quadratic programming. He is particularly well known for his work in parallel sparse matrix factorization, and in heuristics for mixed integer programming.

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