Titlebook: Distributed Optimization, Game and Learning Algorithms; Theory and Applicati Huiwei Wang,Huaqing Li,Bo Zhou Book 2021 The Editor(s) (if app

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The Statesman‘s Year-Book, 1996-7I) system inequality, the algorithm is proved to be able to linearly converge to the exact optimal solution. Furthermore, the ranges of step-size and momentum parameter are characterized, respectively. Finally, simulation results illustrate effectiveness of the method and correctness of theoretical analysis.

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昆虫

Great Britain and Northern Irelandp-size by utilizing Bregman-distance functions. Moreover, we provide sharp bounds on the convergence rates as a function of the network size and topology embodied in the inverse spectral gap. Finally, we present a numerical example to evaluate our algorithm and compare its performance with several similar algorithms.

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The Statesman‘s Year-Book, 1996-7ns and computations is proposed to solve such a problem. By employing iterative inequality techniques, the proposed DPDRPS algorithm is proved to be convergent almost surely. Finally, a numerical example is given to illustrate the effectiveness of the method and the correctness of theoretical analysis.

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oduces the latest distributed optimization, game, and learniThis book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality

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Book 2021 many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response man

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The Statesman‘s Year-Book, 1996-7t functions are considered as strictly convex and strongly convex functions respectively with convergence rate of . and .. Simulation results are provided to validate the availability of the algorithm.

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The Statesman‘s Year-Book, 1996-7h guarantee the existence of the mixed-strategy NE. By the strict diagonal concavity of the regularized Lagrange function, the uniqueness of NE is also guaranteed. Finally, an adaptive learning algorithm is provided to generate the strategy probability distribution for seeking the mixed-strategy NE.

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The Statesman‘s Year-Book, 1996-7d based on the Nguyen–Dupuis network, the experimental results not only demonstrate the correctness of theoretical results, but also show that the B–M RL-based solution method outperforms several existing solution methods.

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