Acknowledgements

It takes a lot of talent and dedication to build the AI products that drive our mission of connecting the world’s professionals to make them more productive and successful. We would like to thank the following folks for their instrumental support in the development of DuaLip:

Pytorch-based DuaLip Solver

We thank the following contributors for their work on the design, implementation, and evaluation of the Pytorch-based DuaLip solver, including algorithmic development, system architecture, and experimental validation:

• Gregory Dexter

• Aida Rahmattalabi

• Sangana Garg

• Qingquan Song

• Zhipeng Wang

Spark-based DuaLip Solver

We acknowledge the contributors to the original Spark-based DuaLip solver for establishing the core ideas, Spark-based implementations, and experimental groundwork that informed and enabled the Pytorch-based DuaLip solver redesign:

• Rohan Ramanath

• Kinjal Basu

• Yao Pan

• Miao Cheng

• Sathiya Keerthi

• Amol Ghoting

• Konstantin Salomatin

• Kenneth Tay

• Borja Ocejo

• Ayan Acharya

We also thank the following individuals and teams for their support through detailed and thoughtful discussions, infrastructure, technical review, or advisory contributions:

• Shaunak Chatterjee

• Ankan Saha

• Souvik Ghosh

• Shenyinying (Ruby) Tu

• Yuan Gao

• Deepak Kumar

• Lingjie Weng

• Shipeng Yu

• Hema Raghavan

• Romer Rosales

• Suju Rajan

• Igor Perisic

Additional Support

Finally, we would also like to thank Rahul Mazumder for the great collaboration during the algorithmic development of the solver across both Spark-based and Pytorch-based versions of the solver.