Palma London

Palma London

(she/her/hers)

Caltech

Convex Optimization, Distributed Algorithms, Machine Learning

Palma London received her Ph.D. and M.Sc. in Computer Science at Caltech. She received her B.S.E.E. in Electrical Engineering and B.S. in Mathematics at the University of Washington. She is currently a postdoctoral researcher at Cornell. Her research broadly spans convex optimization, machine learning and distributed algorithms. 

A parallelizable acceleration framework for packing linear programs

This talk presents an acceleration framework for packing linear programming problems where the amount of data available is limited, i.e., where the number of constraints m is small compared to the variable dimension n. The framework can be used as a black box to speed up linear programming solvers dramatically, by two orders of magnitude in our experiments. We present worst-case guarantees on the quality of the solution and the speedup provided by the algorithm, showing that the framework provides an approximately optimal solution while running the original solver on a much smaller problem. The framework can be used to accelerate exact solvers, approximate solvers, and parallel/distributed solvers. Further, it can be used for both linear programs and integer linear programs.