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This book provides easy access to the basic principles and methods for solving constrained and unconstrained convex optimization problems. Included are sections that cover: basic methods for solving constrained and unconstrained optimization problems with differentiable objective functions, convex sets and their properties, convex functions and their properties and generalizations, and basic principles of sub-differential calculus and convex p...

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Description of methods of estimation of linear functionals of the unknown values of vector-valued stationary stochastic sequences and processes is presented. Extrapolation, interpolation and filtering problems are investigated. Two main approaches to solution of the estimation problems are developed. The first one, the spectral certainty case, is based on the assumption that matrices of spectral densities of stochastic sequences and processes ...

Description of methods of estimation of linear functionals depending on the unknown values of periodically correlated random processes based on observations of the processes and noise is presented. The crucial assumption in application of traditional methods of finding solution to the estimation problem for random processes is that spectral densities of processes are exactly known. However, in practical situations complete information on spect...