Starting from where a first course in convex optimization leaves
off, this text presents a unified analysis of first-order
optimization methods - including parallel-distributed algorithms -
through the abstraction of monotone operators. With the increased
computational power and availability of big data over the past
decade, applied disciplines have demanded that larger and larger
optimization problems be solved. This text covers the first-order
convex optimization methods that are uniquely effective at solving
these large-scale optimization problems. Readers will have the
opportunity to construct and analyze many well-known classical and
modern algorithms using monotone operators, and walk away with a
solid understanding of the diverse optimization algorithms.
Graduate students and researchers in mathematical optimization,
operations research, electrical engineering, statistics, and
computer science will appreciate this concise introduction to the
theory of convex optimization algorithms.
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