Optimization and Its Applications in Control and Data Sciences In Honor of Boris T. Polyak's 80th Birthday /

This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference "Optimization and Its Applications in Control and Data Science" dedicated to Professor Boris T. Polyak, which was held in M...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Goldengorin, Boris (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:Springer Optimization and Its Applications, 115
Temas:
Mathematical optimization.
Artificial intelligence-Data processing.
System theory.
Control theory.
Dynamical systems.
Algorithms.
Computer science-Mathematics.
Optimization.
Data Science.
Systems Theory, Control .
Dynamical Systems.
Mathematical Applications in Computer Science.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Introduction: Big, Small, and Optimal Steps of Boris Polyak (Boris Goldengorin)
  • A Convex Optimization Approach to Modeling of Stationary Periodic Time Series (Anders Lindquist and Giorgio Picci)
  • New two-phase proximal method of solving the solving the problem of equilibrium programming (Sergey I. Lyashko and Vladimir V. Semenov)
  • Minimax Control of Positive Switching Systems with Markovian Jumps (Patrizio Colaneri, José Geromel, Paolo Bolzern, Grace Deaecto)
  • A modified Polak-Ribière-Polyak conjugate gradient algorithm with sufficient descent and conjugacy properties for unconstrained optimization (Neculai Andrei)
  • Subgradient method with the transformation of space and Polyak's step (Petro Stetsyuk)
  • Invariance Conditions for Nonlinear Dynamical Systems (Y. Song, and T. Terlaky)
  • Nonparametric ellipsoidal approximation of compact sets of random points (S. I., Lyashko, V.V. Semenov D.A. Klyushin, M.V. Prysyazhna, M.P. Shlykov)
  • Algorithmic Principle of the Least Excessive Revenue for finding market equilibria (Yurii Nesterov, Vladimir Shikhman)
  • Matrix-Free Convex Optimization Modeling (Stephen Boyd and Steven Diamond)
  • Stochastic Optimization and Statistical Learning in Reproducing Kernel Hilbert Spaces the Stochastic Quasi-Gradient Methods (Vladimir I. Norkin). .

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