other · 2021

Convex Programming: Algorithms

Michael H. Veatch

Cited by 0Abstract and details only

Abstract

Solving convex programs is a broad and more advanced topic. This final chapter provides a brief introduction and considers the convex optimization problem. It begins by presenting some motivating examples. The chapter describes the easier special case of separable programs, making a connection with linear programming approximations. It discusses the unconstrained convex programs. Another important class of problems, quadratic programs, is also introduced. For unconstrained problems, Newton’s method is equivalent to optimizing a quadratic function at each iteration. This relationship can be extended to problems with equality constraints. The chapter also presents the main algorithm: a primal-dual interior point method. While there are many algorithms for constrained convex programs, the primal-dual interior point algorithm performs well on many types of problems and has a good theoretical rate of convergence.

Cite this paper

APA 7

Veatch, M. H. (2021). Convex Programming: Algorithms. https://doi.org/10.1002/9781119664079.ch15

MLA 9

Veatch, Michael H. “Convex Programming: Algorithms.” 2021, pp. 317–42, https://doi.org/10.1002/9781119664079.ch15.

Chicago

Veatch, Michael H. 2021. “Convex Programming: Algorithms.” https://doi.org/10.1002/9781119664079.ch15.

Harvard

Veatch, M.H. (2021) ‘Convex Programming: Algorithms’. Available at: https://doi.org/10.1002/9781119664079.ch15.

BibTeX

@misc{veatch2021convex,
  title = {{Convex Programming: Algorithms}},
  author = {Veatch, Michael H.},
  pages = {317--342},
  year = {2021},
  doi = {10.1002/9781119664079.ch15}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.