MARC details
| 000 -LEADER |
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| fixed length control field |
02582 a2200217 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
231110b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781108741774 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
515.882 VIS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Vishnoi, Nisheeth K. |
| 245 ## - TITLE STATEMENT |
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| Title |
Algorithms for convex optimization |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New York: |
| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2021. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvi, 323p. |
| Other physical details |
ill.:pbk.: |
| Dimensions |
23cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes Bibliography and Index |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Convex optimization studies the problem of minimizing a convex function over a convex set. Convexity, along with its numerous implications, has been used to come up with efficient algorithms for many classes of convex programs. Consequently, convex optimization has broadly impacted several disciplines of science and engineering.<br/><br/>In the last few years, algorithms for convex optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. The fastest known algorithms for problems such as maximum flow in graphs, maximum matching in bipartite graphs, and submodular function minimization, involve an essential and nontrivial use of algorithms for convex optimization such as gradient descent, mirror descent, interior point methods, and cutting plane methods. Surprisingly, algorithms for convex optimization have also been used to design counting problems over discrete objects such as matroids. Simultaneously, algorithms for convex optimization have become central to many modern machine learning applications. The demand for algorithms for convex optimization, driven by larger and increasingly complex input instances, has also significantly pushed the state of the art of convex optimization itself.<br/><br/>The goal of this book is to enable a reader to gain an in depth understanding of algorithms for convex optimization. The emphasis is to derive key algorithms for convex optimization from first principles and to establish precise running time bounds in terms of the input length. Given the broad applicability of these methods, it is not possible for a single book to show the applications of these methods to all of them. This book shows applications to fast algorithms for various discrete optimization and counting problems. The applications selected in this book serve the purpose of illustrating a rather surprising bridge between continuous and discrete optimization.<br/><br/>https://convex-optimization.github.io/ |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Mathematics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Linear Programming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Convex Programming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Algorithms |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Convex Optimization |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Item type |
Books |
| Source of classification or shelving scheme |
Dewey Decimal Classification |
