| Algorithms for Convex Optimization (Cambridge University Press) |
| Friday, 08 October 2021 | |||
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This book looks at how algorithms for convex optimization have become important in algorithm design for both discrete and continuous optimization problems. Nisheeth K. Vishnoi considers their use for problems like maximum flow, maximum matching, and submodular function minimization, and shows how the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. <ASIN:1108741770> The aim is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. Author: Nisheeth K. Vishnoi
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