This comprehensive reference brings readers to the frontier of research on bandit convex optimization or zeroth-order convex optimization. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimization and online learning, including gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery. The book features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers.
This self-contained reference brings readers to the frontier of research on bandit convex optimization while presenting fundamental tools from convex optimization such as gradient-based algorithms, interior point methods and cutting plane methods.
