109 citations · 198 across the 6 of their papers we have counts for
6 papers
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs
Agniva Chowdhury, Gregory Dexter, Palma London +2
Linear programming (LP) is an extremely useful tool which has been successfully applied to solve various problems in a wide range of areas, including operations research, engineeri…
Speeding up Linear Programming using Randomized Linear Algebra
Agniva Chowdhury, Palma London, Haim Avron +1
Linear programming (LP) is an extremely useful tool and has been successfully applied to solve various problems in a wide range of areas, including operations research, engineering…
A Parallelizable Acceleration Framework for Packing Linear Programs
Palma London, Shai Vardi, Adam Wierman +1
This paper 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 smal…
Joint Data Purchasing and Data Placement in a Geo-Distributed Data Market
Xiaoqi Ren, Palma London, Juba Ziani +1
This paper studies two design tasks faced by a geo-distributed cloud data market: which data to purchase (data purchasing) and where to place/replicate the data for delivery (data…
Learning Graphical Models With Hubs
Kean Ming Tan, Palma London, Karthik Mohan +3
We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes. Many authors have studied the use of an…
Node-Based Learning of Multiple Gaussian Graphical Models
Karthik Mohan, Palma London, Maryam Fazel +2
We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motiv…