Optimization And Learning Via Stochastic Gradient Search - Bernd Heidergott

An Introduction To Gradient-based Stochastic Optimization That Integrates Theory And Implementationthis Book Explains Gradient-based Stochastic Optimization, Exploiting The Methodologies Of Stochastic Approximation And Gradient Estimation. Although The Approach Is Theoretical, The Book Emphasizes Developing Algorithms That Implement The Methods. The Underlying Philosophy Of This Book Is That When Solving Real Problems, Mathematical Theory, The Art Of Modeling, And Numerical Algorithms Complement Each Other, With No One Outlook Dominating The Others.the Book First Covers The Theory Of Stochastic Approximation Including Advanced Models And State-of-the-art Analysis Methodology, Treating Applications That Do Not Require The Use Of Gradient Estimation.