An up-to-date account of the interplay between optimization and machine learning, accessible
to students and researchers in both communities. The interplay between optimization and …

Many problems in signal processing and statistical inference involve finding sparse solutions
to under-determined, or ill-conditioned, linear systems of equations. A standard approach …

Linear programming has been the dominant paradigm in optimization since Dantzig's
development of the simplex method in the 1940s. In 1984, the publication of a paper by Karmarkar …

This is a book for people interested in solving optimization problems. Because of the wide (and
growing) use of optimization in science, engineering, economics, and industry, it is …

One of the most effective methods for nonlinearly constrained optimization generates steps
by solving quadratic subproblems. This sequential quadratic programming (SQP) approach …

Finding sparse approximate solutions to large underdetermined linear systems of equations
is a common problem in signal/image processing and statistics. Basis pursuit, the least …

… Liu and Wright [27] consider a version of Algorithm 7 that is the parallel analog of Algorithm
3, in that each update component \(i_k\) is chosen independently and randomly with equal …

The goal of the sparse approximation problem is to approximate a target signal using a linear
combination of a few elementary signals drawn from a fixed collection. This paper surveys …

A distributed model predictive control (MPC) framework, suitable for controlling large-scale
networked systems such as power systems, is presented. The overall system is decomposed …

The modern era of interior-point methods dates to 1984, when Karmarkar proposed his
algorithm for linear programming. In the years since then, algorithms and software for linear …