Logic-based Benders decomposition (LBBD) is presented as a powerful method for tackling large optimization problems by breaking them into manageable components. This comprehensive guide details the theoretical foundations of LBBD, including inference duality and its interplay with stochastic and robust optimization. It also explores practical applications across various domains, showcasing how LBBD can significantly reduce solution times. Additionally, the book reviews existing literature, offering insights and inspiration for applying LBBD to emerging challenges in optimization.
