A control perspective for centralized and distributed convex optimization

By Jing Wang, Nicola Elia. IEEE Conference on Decision and Control and European Control Conference, 2011

408 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/47bab9bbfdde51aaa89f2c8c56400581a78d3d97

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In Computer Networks and Communications

Citations in the corpus

Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems (2015, methodology): “The result can be regarded as an extension of some existing distributed optimization algorithms ( Wang & Elia (2011), Kia, Cort´es, & Martinez (2015), Qiu, Liu, & Xie (2014), and Liu & Wang (2015)) and an application of projected dynamics for variational inequalities (Gao (2003) and Xia & Wang…”
Distributed Continuous-Time Convex Optimization on Weight-Balanced Digraphs (2012, background): “, not necessarily differentiable) and convex, extending the res ults available in the literature [13] for continuously differentiable, strictly convex functions.”
A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems (2017, methodology): “First proposed in [131] with more recent treatments in [132] and [133], the Gradient Dynamics (GD) technique embeds the KKT conditions for an optimization problem in a dynamical system.”
Distributed Continuous-Time Optimization: Nonuniform Gradient Gains, Finite-Time Convergence, and Convex Constraint Set (2017, background): “Article [12] proposed a novel distributed continuous-time algorithm for distributed convex optimization by introducing a dynamic integrator.”
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication (2014, positive): “Recent work [Gharesifard and Cort´es, 2014, Lu and Tang, 2012, Wang and Elia, 2011, Zanella et al., 2011] has introduced continuous-time dynamical solvers whose convergence properties can be analyzed via classical stability analysis.”
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization (2017, methodology): “Work in [77] provides algorithms for centralized and distributed convex optimization from control perspective, while [78] considers an event-triggered distributed optimization for sensor networks.”
Distributed Continuous-Time Convex Optimization With Time-Varying Cost Functions (2015, background): “In [9], a general approach is given to address the problem of distributed convex optimization with equality and inequality constraints.”
Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD (2018, positive): “It is known in [14], [15] that under a certain set of assumptions, the continuous gradient dynamics dθ̄ dt = −∇θ̄L(θ̄, v̄, μ̄, w̄), dv̄ dt = −∇v̄L(θ̄, v̄, μ̄, w̄), dμ̄ dt = −∇μ̄L(θ̄, v̄, μ̄, w̄), dw̄ dt = ∇w̄L(θ̄, v̄, μ̄, w̄) of the Lagrangian function can solve the saddle point problem.”
Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms (2013, methodology): “We refer to the four methods as: 1) deterministic Jacobi-type; 2) deterministic gradient; 3) randomized GaussSeidel-type; and 4) randomized gradient-type (see Section II for the algorithms’ details.)”
A Passivity-Based Approach to Nash Equilibrium Seeking Over Networks (2017, methodology): “, with an integrator or proportional-integrator term through L, which preserves passivity, as in [37].”
A survey of distributed optimization (2019, methodology): “Note that algorithm (10) can also be interpreted as a primal-dual algorithm as shown in Gharesifard and Cortés (2014) ; Wang and Elia (2011) and Dörﬂer (2019) .”