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- Stephen P. Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein
- Foundations and Trends in Machine Learning
- 2011

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it isâ€¦ (More)

- Stephen P. Boyd, Arpita Ghosh, Balaji Prabhakar, Devavrat Shah
- IEEE Transactions on Information Theory
- 2006

Motivated by applications to sensor, peer-to-peer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in anâ€¦ (More)

- Lieven Vandenberghe, Stephen P. Boyd
- SIAM Review
- 1996

In semidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear andâ€¦ (More)

- Lin Xiao, Stephen P. Boyd
- Systems & Control Letters
- 2004

We consider the problem of !nding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at theâ€¦ (More)

It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done byâ€¦ (More)

It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done byâ€¦ (More)

- Wei Yu, Wonjong Rhee, Stephen P. Boyd, John M. Cioffi
- IEEE Transactions on Information Theory
- 2004

This paper proposes an efficient numerical algorithm to compute the optimal input distribution that maximizes the sum capacity of a Gaussian multiple-access channel with vector inputs and a vectorâ€¦ (More)

- Neal Parikh, Stephen P. Boyd
- Foundations and Trends in Optimization
- 2014

This monograph is about a class of optimization algorithms called proximal algorithms. Much like Newtonâ€™s method is a standard tool for solving unconstrained smooth optimization problems of modestâ€¦ (More)

A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solveâ€¦ (More)

- Lin Xiao, Stephen P. Boyd, Sanjay Lall
- IPSN 2005. Fourth International Symposium onâ€¦
- 2005

We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributedâ€¦ (More)