Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize

Summary

Grab Senior Data Scientist Dr. Liuqin Yang, Professor Defeng Sun, and Professor Kim-Chuan Toh received the 2018 Beale-Orchard-Hays Prize for their research paper introducing SDPNAL+. The software employs a majorised semismooth Newton-CG augmented Lagrangian method to solve large-scale semidefinite programming problems with nonnegative constraints. While traditional methods struggled beyond matrix dimensions of 2,000 and 5,000 constraints, SDPNAL+ successfully scales to matrix dimensions of 9,261 and over 12 million constraints. In benchmark testing, the software solved a problem on a desktop PC in 1.5 hours that required 122 hours on a 56-core CPU and 128-GPU cluster using a traditional solver. Grab implements these optimisation techniques to accelerate its passenger-driver allocation algorithms by hundreds of times.

Context

Traditional optimisation methods could only solve small and medium scale semidefinite programming problems with matrix dimensions under 2,000 and fewer than 5,000 constraints.

Approach / What changed

Researchers developed SDPNAL+, software implementing a majorised semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints.

Takeaways

• SDPNAL+ expanded solvable semidefinite programming limits to matrix dimensions of 9,261 and over 12 million constraints.
• In a benchmark test, SDPNAL+ completed a problem in 1.5 hours on a standard desktop PC, compared to 122 hours required by a traditional solver on a 56-core CPU and 128-GPU cluster.
• Grab implements these mathematical optimisation algorithms within its driver-passenger allocation system to run computation tasks hundreds of times faster.

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