The average-case analysis of numerical problems is the counterpart of the more traditional worst-case approach. The analysis of average error and cost leads to new insight on numerical problems as well as to new algorithms. The book provides a survey of results that were mainly obtained during the last 10 years and also contains new results. The problems under consideration include approximation/optimal recovery and numerical integration of univariate and multivariate functions as well as zero-finding and global optimization. Background material, e.g. on reproducing kernel Hilbert spaces and random fields, is provided. Linear problems: Definitions and a classical example.- Second-order results for linear problems.- Integration and approximation of univariate functions.- Linear problems for univariate functions with noisy data.- Integration and approximation of multivariate functions.- Nonlinear methods for linear problems.- Nonlinear problems. "The monograph is carefully written and gives a systematic up-to-date treatment of theoretical questions concerning the average-case analysis of numerical problems. It is helpful for both theoretical research and practical applications." (Karel Najzar, zbMATH 0949.65146, 2022)
