Convex optimization problems arise frequently in many different fields. This book provides a
comprehensive introduction to the subject, and shows in detail how such problems can be …

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 …

This monograph is about a class of optimization algorithms called proximal algorithms.
Much like Newton’s method is a standard tool for solving unconstrained smooth optimization …

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 …

… diamond@cs.stanford.edu Stephen Boyd boyd@stanford.edu Departments of Computer …
Boyd. Conic optimization via operator splitting and homogeneous self-dual embedding. Journal …

The basic topic of this book is solving problems from system and control theory using convex
optimization. We show that a wide variety of problems arising in system and control theory …

We describe graph implementations, a generic method for representing a convex function
via its epigraph, described in a disciplined convex programming framework. This simple and …

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 …

If you are actively using CVX in teaching, research, or applications, and haven’t yet told us
about it, please do so! Drop us an email. It is truly encouraging to hear about new uses for …

In a second-order cone program (SOCP) a linear function is minimized over the intersection
of an affine set and the product of second-order (quadratic) cones. SOCPs are nonlinear …