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Large-scale optimization as a book category covers methods, theory, and applications for solving optimization problems that are too large, high-dimensional, or data-intensive for classical techniques, emphasizing scalability, efficiency, and practical implementation. These books treat topics such as first- and second-order algorithms adapted for big problems (gradient, stochastic gradient, coordinate descent, quasi-Newton, interior-point and trust-region methods), decomposition and distributed/parallel optimization, randomized and streaming algorithms, convex and nonconvex formulations, regularization and sparsity, numerical linear algebra and preconditioning, convergence and complexity analysis, and software and systems considerations (memory, communication, GPU/cluster implementations). They span applications in machine learning, signal processing, operations research, engineering design, and data science, and are aimed at advanced undergraduates, graduate students, researchers, and practitioners who need both theoretical tools and practical guidance for designing and implementing optimization solutions at scale.

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