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"Estimation and Optimization" is a cross-disciplinary book category focused on mathematical and computational methods for inferring unknown quantities and finding best solutions under constraints. Books in this genre cover statistical and signal-processing estimation (parameter inference, Bayesian and frequentist methods, filtering and state estimation), deterministic and stochastic optimization (convex and nonconvex theory, linear/quadratic programming, global and heuristic techniques), numerical algorithms, and practical implementations for applications in engineering, data science, control, operations research, and economics. They typically blend theory (modeling, convergence, optimality conditions) with algorithm design, performance analysis, and real-world case studies or software examples to equip readers with tools for decision-making, parameter identification, and resource allocation. Suitable for advanced undergraduates, graduate students, and practitioners, the category spans introductory texts to specialized monographs on topics such as Kalman filtering, Bayesian inference, convex optimization, and large-scale machine learning optimization.

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