Optimization and Its Applications in Control and Data Sciences
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Optimization and Its Applications in Control and Data Sciences
In Honor of Boris T. Polyak's 80th Birthday
Goldengorin, Boris
Springer International Publishing AG
06/2018
507
Mole
Inglês
9783319824901
15 a 20 dias
801
Descrição não disponível.
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, Jose Geromel, Paolo Bolzern, Grace Deaecto).- A modified Polak-Ribiere-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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Data analysis;Dynamical Systems;Linear programming;Markovian Jumps;Nonconvex programming;Nonlinear programming;Nonparametric ellipsoidal approximation;global optimization;data structures
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, Jose Geromel, Paolo Bolzern, Grace Deaecto).- A modified Polak-Ribiere-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).
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
