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Books > Computing & IT > General theory of computing > Data structures
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Algorithms for Convex Optimization (Hardcover)
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Algorithms for Convex Optimization (Hardcover)
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In the last few years, Algorithms for Convex Optimization have
revolutionized algorithm design, both for discrete and continuous
optimization problems. For problems like maximum flow, maximum
matching, and submodular function minimization, the fastest
algorithms involve essential methods such as gradient descent,
mirror descent, interior point methods, and ellipsoid methods. The
goal of this self-contained book 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. This modern text explains the success of these
algorithms in problems of discrete optimization, as well as how
these methods have significantly pushed the state of the art of
convex optimization itself.
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