Algorithms for Convex Optimization
In recent years, convex optimization algorithms have revolutionized algorithm design, for both discrete and continuous optimization problems. For problems like maximum throughput, maximal matching and modular submimal minimization, the fastest algorithms involve essential methods like gradient descent, mirror descent, inside point methods and the ellipsoid method. The goal of this standalone book is to provide researchers and professionals in computer science, data science, and machine learning with an in-depth understanding of these algorithms. The text emphasizes how to find the key algorithms for convex optimization from first principles and how to set the correct runtime limit. This modern text explains the success of these algorithms in discrete optimization problems, as well as how these methods have dramatically advanced the state of the art of convex optimization.
Algorithms for Convex Optimization epub
Author(s): Nisheeth K. Vishnoi
Publisher: Cambridge University Press, Year: 2021
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