In this tutorial we give an overview of the basic ideas underlying Support Vector (SV)
machines for function estimation. Furthermore, we include a summary of currently used …

We provide a comprehensive review of five representative ℓ 1-minimization methods, ie,
gradient projection, homotopy, iterative shrinkage-thresholding, proximal gradient, and …

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

This book is intended as a text covering the central concepts of practical optimization
techniques. It is designed for either self-study by professionals or classroom work at the …

A new methodology for constructing convex optimization models called disciplined convex
programming is introduced. The methodology enforces a set of conventions upon the …

Interior point algorithms for solving linear programs have been studied extensively for a long
time [eg Karmarkar 1984; Lee, Sidford FOCS'14; Cohen, Lee, Song STOC'19]. For linear …

Linear Programming: Foundations and Extensions Page 1 useful details of specific
implementations. The problems discussed arise in telecommunications, computer vision (point …

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 …

This paper gives an approach to implementing a second-order primal-dual interior point
method. It uses a Taylor polynomial of second order to approximate a primal-dual trajectory …

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 …