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This course covers basic theory and algorithms for unconstrained and constrained optimization problems, convex and non-convex optimization problems, optimality conditions including duality theory. Algorithms include basic first-order and second-order methods. Some applications of optimization, such as those in data science, will be introduced.
This book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
