A tutorial on decomposition methods for network utility maximization

By D. P. Palomar, Mung Chiang. IEEE Journal on Selected Areas in Communications, 2006

1,663 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/5d09b997a95a0f82744e185be127397999f60a32

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Citations in the corpus

Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks (2019, background): “…fact that data is persistently arriving in a sequential fashion [3], that it is physically decentralized across an interconnected network, and that the nodes of the network may correspond to disparate classes of objects (such as users and a base station) with different time-scale requirements [4].”
On Ergodic Sum Capacity of Fading Cognitive Multiple-Access and Broadcast Channels (2008, methodology): “ then be obtained from these KKT optimality conditions: 3Note that this method has been recently applied broadly to resource allocation problems for communication systems (see, e.g., a tutorial paper [39] and references therein). In particular, this method has been applied to multiuser resources all”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, methodology): “For the same aim and inspired by the Network Utility Maximization (NUM) [115] framework, D’Aronco et al. [116] proposed a distributed price-based, network-assisted HAS system for multiple concurrent HAS clients sharing a common bottleneck.”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, methodology): “The Gauss-Seidel iteration, the Jacobi iteration, or an entirely asynchronous iteration may be adopted to this end [18].”
Robust Energy Management for Microgrids With High-Penetration Renewables (2012, background): “The features of strong duality and separability imply that Lagrangian relaxation and dual decomposition are applicab le to yield a decentralized algorithm; see also related techni ques in power systems [22] and communication networks [23], [24] .”
A Survey of Decomposition Methods for Multi-objective Optimization (2014, neutral): “In this case, it is possible to optimize f1 and f2 in separately optimization problems [7–10].”
D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization (2016, methodology): “Under the assumption of a star network topology, [20] focuses on solving the dual problem of (1) using a dual decomposition like technique [21].”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, methodology): “, a tutorial paper [44]) is a general method to deal with this type of average constraints for optimization over a number of parallel dimensions, which is explained as follows.”
Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, methodology): “The latter can be viewed as a network utility maximization [40, 53, 131, 194], thus we can utilize many of the results on distributed optimization that has”
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems (2018, methodology): “the dual decomposition method [42]), successive convex approximation methods are known to converge fast to close-to-optimal solutions [43], [44].”
Spatially Sparse Precoding in Millimeter Wave MIMO Systems (2013, background): “Unfortunately, finding global optima for similar constrained joint optimization problems is often found to be intractable [64], [65].”
Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms (2011, methodology): “Moreover, we plan to evaluate these algorithm in an EnHANTs testbed that we are currently building [10].”
Performance Isolation and Fairness for Multi-Tenant Cloud Storage (2012, neutral): “Pisces’s algorithms and its four part decomposition can be conceptually derived as a distributed optimization problem [24].”
Sensing-Based Spectrum Sharing in Cognitive Radio Networks (2008, methodology): “Therefore, the dual decomposition method illustrated in [9] can be applied to solve this problem.”
A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks (2017, methodology): “mobile sink model [130], [146] using GNU linear”
Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures (2007, background): “The following lemma [105] characterizes the subgradient of the master problem at the top level.”