Convex-optimization / Algorithms for Convex Optimization – 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. 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. 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. The intended audience includes advanced undergraduate students, graduate students, and researchers from theoretical computer science, discrete optimization, and machine learning.

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Title	Algorithms for Convex Optimization – 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> The intended audience includes advanced undergraduate students, graduate students, and researchers from theoretical computer science, discrete optimization, and machine learning.
Description	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. 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. 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. The intended audience includes advanced undergraduate students, graduate students, and researchers from theoretical computer science, discrete optimization, and machine learning.
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