Efficient Vertex-Oriented Polytopic Projection for Web-Scale Applications


  • Rohan Ramanath LinkedIn Corporation
  • S. Sathiya Keerthi LinkedIn Corporation
  • Yao Pan LinkedIn Corporation
  • Konstantin Salomatin LinkedIn Corporation
  • Kinjal Basu LinkedIn Corporation




Constraint Satisfaction And Optimization (CSO), Machine Learning (ML), Domain(s) Of Application (APP)


We consider applications involving a large set of instances of projecting points to polytopes. We develop an intuition guided by theoretical and empirical analysis to show that when these instances follow certain structures, a large majority of the projections lie on vertices of the polytopes. To do these projections efficiently we derive a vertex-oriented incremental algorithm to project a point onto any arbitrary polytope, as well as give specific algorithms to cater to simplex projection and polytopes where the unit box is cut by planes. Such settings are especially useful in web-scale applications such as optimal matching or allocation problems. Several such problems in internet marketplaces (e-commerce, ride-sharing, food delivery, professional services, advertising, etc.), can be formulated as Linear Programs (LP) with such polytope constraints that require a projection step in the overall optimization process. We show that in some of the very recent works, the polytopic projection is the most expensive step and our efficient projection algorithms help in gaining massive improvements in performance.




How to Cite

Ramanath, R., Keerthi, S. S., Pan, Y., Salomatin, K., & Basu, K. (2022). Efficient Vertex-Oriented Polytopic Projection for Web-Scale Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 3821-3829. https://doi.org/10.1609/aaai.v36i4.20297



AAAI Technical Track on Constraint Satisfaction and Optimization