Last edited by Arashimuro

Monday, February 3, 2020 | History

5 edition of **Convex Analysis and Minimization Algorithms: Part 2** found in the catalog.

- 163 Want to read
- 34 Currently reading

Published
**October 25, 2001** by Springer .

Written in English

- Applied mathematics,
- Calculus of variations,
- Mathematics,
- Calculus,
- Linear Programming,
- Number Systems,
- Business & Economics / Operations Research,
- Convex Analysis,
- Mathhematical Programming,
- Nonsmooth Optimization,
- Numerical Algorithms,
- Optimization

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 360 |

ID Numbers | |

Open Library | OL9061028M |

ISBN 10 | 3540568522 |

ISBN 10 | 9783540568520 |

Crossref Approximation of linear programs by Bregman's DF projections. Crossref On the ergodic convergence rates of a first-order primal—dual algorithm. A final section is devoted to nonconvex problems and estimates of the duality gap, with special focus on separable problems. Example problems in statistics, signal and image processing, control theory

Convex Optimization by S. Students can also define their own project after communicating with the instructor. Its current contents are as follows: Chapter 6 on Algorithms: 6. Crossref Split Bregman method for the modified lot model in image denoising.

Advances in Optimization and Approximation, Textbooks: We will not require a textbook. Numerical Functional Analysis and Optimization Necessary conditions for optimality[ edit ] One of Fermat's theorems states that optima of unconstrained problems are found at stationary pointswhere the first derivative or the gradient of the objective function is zero see first derivative test. Automatica 55, Crossref Block coordinate proximal gradient methods with variable Bregman functions for nonsmooth separable optimization.

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Series: Mathematical programming study ; 1. Sufficient conditions for optimality[ edit ] While the first derivative test identifies points that might be extrema, this test does not distinguish a point that is a minimum from one Convex Analysis and Minimization Algorithms: Part 2 book is a maximum or one that is neither.

Crossref Reconstructing conductivity coefficients based on sparsity regularization and measured data in electrical impedance tomography. Numerical Linear Algebra with Applications Bertsekas, Athena Scientific.

Algorithmic Descent; 6. Chamberlain, M. Section 3. Crossref An introduction to continuous optimization for imaging. Walter Murray, Margaret H. Crossref A proximal multiplier method for separable convex minimization.

Crossref Interior point methods for equilibrium problems. Other types of optimization problems, such as those arising in Fenchel duality, are also part of our scope.

Operations Research Letters Prior knowledge of linear and nonlinear optimization theory is not assumed, although it will undoubtedly be helpful in providing context and perspective. Journal of Inequalities and Applications Crossref A derivative-free comirror algorithm for convex optimization.

In other words, defining the problem as multi-objective optimization signals that some information is missing: desirable objectives are given but combinations of them are not rated relative to each other.

Algorithms for constrained minimization of smooth nonlinear functions. The book was quite extensive, was structured at least in part as a research monograph, and aimed to bridge the gap between convex and nonconvex optimization using concepts of nonsmooth analysis. It discusses its connection with conjugacy theory, and it charts its applications to constrained optimization and minimax problems.

Numerical Functional Analysis and Optimization It touches upon nearly all major aspects of the subject, and it is sufficient for the development of the core analytical issues of convex optimization. Chapters Table of contents 9 chapters.This book, developed through class instruction at MIT over the last 15 years, provides an accessible, concise, and intuitive presentation of algorithms for solving convex optimization problems.

It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that. Continuation of Convex Optimization I.

Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections.

Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization.

Selected applications in areas such as control, circuit design. CONVEX ANALYSIS AND NONLINEAR OPTIMIZATION Theory and Examples appeared recently.

Hiriart-Urruty and Lemar´echal’s Convex Analysis and Minimization Algorithms [86] is a comprehensive but gentler introduction. Our goal is not to supplant these works, but on the contrary in part, like this book to be an entr´ee for mathemati-cians to.As such, it can easily be integrated into a pdf study curriculum.

Minimization algorithms, more specifically those adapted to non-differentiable functions, provide an immediate application of convex analysis to various fields related to optimization and operations research.图书Convex Analysis and Minimization Algorithms 介绍、书评、论坛及推荐.

Part 2: Advanced Theory and Bundle Methods (Grundlehren der mathematischen Wissenschaften) (Pt. 2) 出版年: 页数: 定价: USD 装帧: Hardcover ISBN: Convex Analysis and Ebook Algorithms I: Fundamentals (Grundlehren ebook mathematischen Wissenschaften Book ) - Kindle edition by Jean-Baptiste Hiriart-Urruty, Claude Lemarechal.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Convex Analysis and Minimization Algorithms I: .