PrivOpt: an intrinsically private distributed optimization algorithm

Esteki, Amir-Salar; Kia, Solmaz S.

doi:10.23919/acc53348.2022.9867222

Cited by 2 publications

(2 citation statements)

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“…randomly between (0, 0.01) and (2, 2.5), respectively. Cost function ( 14) is smooth and convex, and the optimization problem (14) has infinite number of minimizers that correspond to the minimum cost of f ⋆ = 0.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…Distributed solutions for problem (1) are studied extensively in the literature. For example, in the context of the power generator economic dispatch problem, [11]- [14] offer distributed solutions that solve a special case of (1) when local cost functions are quadratic. Distributed algorithm design with non-quadratic costs are presented in [15]- [17] in discrete-time form, and [18]- [23] in continuous-time form.…”

Section: Introductionmentioning

confidence: 99%

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Distributed optimal resource allocation using transformed primal-dual method

Kia

Wei

Chen

2023

2023 American Control Conference (ACC)

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We consider an in-network optimal resource allocation problem in which a group of agents interacting over a connected graph want to meet a demand while minimizing their collective cost. The contribution of this paper is to design a distributed continuous-time algorithm for this problem inspired by a recently developed first-order transformed primal-dual method. The solution applies to cluster-based setting where each agent may have a set of subagents, and its local cost is the sum of the cost of these subagents. The proposed algorithm guarantees an exponential convergence for strongly convex costs and asymptotic convergence for convex costs. Exponential convergence when the local cost functions are strongly convex is achieved even when the local gradients are only locally Lipschitz. For convex local cost functions, our algorithm guarantees asymptotic convergence to a point in the minimizer set. Through numerical examples, we show that our proposed algorithm delivers a faster convergence compared to existing distributed resource allocation algorithms.

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Section: Numerical Simulationsmentioning

confidence: 99%