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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Goldengorin, Boris (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Σειρά: Springer Optimization and Its Applications, 115
• Θέματα: Mathematics.; Data structures (Computer science).; Dynamics.; Ergodic theory.; System theory.; Computer science > Mathematics.; Computer mathematics.; Algorithms.; Mathematical optimization.; Optimization.; Data Structures.; Systems Theory, Control.; Dynamical Systems and Ergodic Theory.; Mathematical Applications in Computer Science.

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 Moscow, Rus...

Πίνακας περιεχομένων:

• 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). .