The Projected Subgradient Algorithm in Convex Optimization, 1st ed. 2020 SpringerBriefs in Optimization Series
Auteur : Zaslavski Alexander J.
Studies the influence of computational errors for the generalized subgradient projection algorithm
Contains solutions to a number of difficult and interesting problems in the numerical optimization
Useful for experts in applications of optimization, engineering, and economics
Focuses on the subgradient projection algorithm for minimization of convex and nonsmooth functions and for computing the saddle points of convex-concave functions under the presence of computational errors
Date de parution : 11-2020
Ouvrage de 146 p.
15.5x23.5 cm