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Large-Scale and Distributed Optimization


Large-Scale and Distributed Optimization
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Beschreibung

- Large-Scale and Distributed Optimization: An Introduction. - Exploiting Chordality in Optimization Algorithms for Model Predictive Control. - Decomposition Methods for Large-Scale Semidefinite Programs with Chordal Aggregate Sparsity and Partial Orthogonality. - Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization. - Primal-Dual Proximal Algorithms for Structured Convex Optimization: A Unifying Framework. - Block-Coordinate Primal-Dual Method for Nonsmooth Minimization over Linear Constraints. - Stochastic Forward Douglas-Rachford Splitting Method for Monotone Inclusions. - Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints. - Frank-Wolfe Style Algorithms for Large Scale Optimization. - Decentralized Consensus Optimization and Resource Allocation. - Communication-Efficient Distributed Optimization of Self-concordant Empirical Loss. - Numerical Construction of Nonsmooth Control Lyapunov Functions. - Convergence of an Inexact Majorization-Minimization Method for Solving a Class of Composite Optimization Problems.

Eigenschaften

Breite: 155
Gewicht: 650 g
Höhe: 233
Länge: 25
Seiten: 412
Sprachen: Englisch
Autor: Anders Rantzer, Pontus Giselsson

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