Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Sulaiman A. Alghunaim"'
Publikováno v:
IEEE Transactions on Signal Processing. 69:5568-5579
This work considers multi-agent sharing optimization problems, where each agent owns a local smooth function plus a non-smooth function, and the network seeks to minimize the sum of all local functions plus a coupling composite function (possibly non
Autor:
Ali H. Sayed, Sulaiman A. Alghunaim
Publikováno v:
IEEE Transactions on Automatic Control. 65:175-190
This paper develops an effective distributed strategy for the solution of constrained multiagent stochastic optimization problems with coupled parameters across the agents. In this formulation, each agent is influenced by only a subset of the entries
Autor:
Sulaiman A. Alghunaim, Kun Yuan
We study the consensus decentralized optimization problem where the objective function is the average of $n$ agents private non-convex cost functions; moreover, the agents can only communicate to their neighbors on a given network topology. The stoch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f0b711c9fe877f8738066a45c2c7805
Publikováno v:
EUSIPCO
This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::407e041592799a88b43570817384c527
Publikováno v:
CDC
Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution unde
This work studies a class of non-smooth decentralized multi-agent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common non-smooth term. We propose a general primal-dual algorithmic fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77b2e1784aea291a65743c470b892480
http://arxiv.org/abs/1909.06479
http://arxiv.org/abs/1909.06479
Linear Convergence of Primal-Dual Gradient Methods and their Performance in Distributed Optimization
Autor:
Sulaiman A. Alghunaim, Ali H. Sayed
In this work, we revisit a classical incremental implementation of the primal-descent dual-ascent gradient method used for the solution of equality constrained optimization problems. We provide a short proof that establishes the linear (exponential)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b96e7355220b17d2460dc1b341d582d1
http://arxiv.org/abs/1904.01196
http://arxiv.org/abs/1904.01196
Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed {\em deterministic} optimization problems. These methods employ constant step-sizes and converge linearly t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f99b1fb7391df07dfde11cc17cc0213
Publikováno v:
CDC
In this work, a distributed multi-agent optimization problem is studied where different subsets of agents are coupled with each other through affine constraints. Moreover, each agent is only aware of its own contribution to the constraints and only k
Autor:
Ali H. Sayed, Sulaiman A. Alghunaim
Publikováno v:
ICASSP
This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in