Numerical Optimization, Jorge Nocedal and Stephen J. Wright, 2006 (Springer)DOI: 10.1007/978-0-387-40065-5 - Classic textbook providing comprehensive coverage of Newton's method, its computational issues, and theoretical aspects.
Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Discusses the practical limitations of Newton's method for large-scale deep learning models, including computational and storage burdens.
Convex Optimization, Stephen Boyd and Lieven Vandenberghe, 2004 (Cambridge University Press) - Foundational text covering the theoretical underpinnings of Newton's method, including the positive definiteness requirement.