In this work, we propose a sketching-based central path method for solving linear
programmings, whose running time matches the state of the art results [Cohen, Lee, Song …

Large-scale optimization problems that seek sparse solutions have become ubiquitous.
They are routinely solved with various specialized first-order methods. Although such …

This paper develops a scalable new algorithm, called NysADMM, to minimize a smooth
convex loss function with a convex regularizer. NysADMM accelerates the inexact …

We consider the problem of encoding a set of vectors into a minimal number of bits while
preserving information on their Euclidean geometry. We show that this task can be …

Large-scale continuous optimization arises in many practical problems such as machine
learning, signal processing, and imaging. It is usually challenging to analyze the theoretical …

We present novel, efficient algorithms for solving extremely large optimization problems. A
significant bottleneck today is that as the size of datasets grow, researchers across …

We present novel, efficient algorithms for solving extremely large optimization problems. A
significant bottleneck today is that as the size of datasets grow, researchers across …