Distributed Function Minimization in Apache Spark

This work provides a tool for distributed optimization in big data contexts, but it is incremental as it adapts existing methods to a new platform without novel algorithmic contributions.

The authors tackled the problem of distributed function minimization by implementing gradient and quasi-Newton methods in Apache Spark, showcasing it with applications like Optimal Transport and scalability tests on classification and regression tasks, but no concrete performance numbers are provided.