This book introduces the theory and methods of robust optimization. It addresses the challenge of making decisions under uncertainty, focusing on how to design solutions that perform well even when parameters deviate from their nominal values. The authors, leading researchers in the field, present a unified framework for robust optimization, drawing on concepts from convex analysis, duality theory, and computational linear algebra.

The book is regarded as a comprehensive and authoritative treatment of robust optimization. It is recognized for its rigorous mathematical exposition and its broad coverage of theoretical concepts and practical applications. The text is suitable for graduate students and researchers in optimization, operations research, and related fields seeking a deep understanding of decision-making under uncertainty.