This book offers a timely and rigorous contribution at the intersection of numerical linear algebra, optimization, and modern data-science applications. The manuscript stands out for its balanced integration of theoretical foundations and computational practice. It develops core concepts in numerical linear algebra such as matrix factorizations, eigenvalue problems, and iterative methods, while systematically connecting them to optimization techniques central to data science, including gradient-based methods, convex and non-convex optimization and large-scale algorithms. The book includes a strong emphasis on contemporary applications.

Khalide Jbilou is a full professor in the Laboratory of Pure and Applied Mathematics (LMPA) at the Université du Littoral Côte d’Opale (ULCO), France. He received his Ph.D. in Numerical Analysis from the University of Lille and obtained his Habilitation (Habilitation à Diriger des Recherches) from ULCO in 1999. Professor Jbilou has authored more than 220 papers in leading international peer-reviewed journals and serves on the editorial boards of several international scientific journals. He has also acted as Managing Guest Editor or Guest Editor for more than 16 special issues in high-quality international journals and has co-edited two Springer conference proceedings.

Marcos Raydan received his Ph.D. in computational and applied mathematics from Rice University in Houston, Texas, in 1991. Following a one-year postdoctoral fellowship at the University of Kentucky, he held academic positions at two universities in Caracas, Venezuela (UCV and USB) and visiting appointments at numerous universities in Europe, South America, and North America. For the past seven and a half years, he has been a principal researcher at the Center of Mathematics and Applications (NOVA Math) at NOVA University in Lisbon, Portugal. Professor Raydan has authored over 100 papers in leading international, peer-reviewed journals and two books on scientific computing.