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researcher

Peng Yi

25 papers hereh-index 242.8k citations74 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author14
  • last author4

Across the 24 of 25 papers where every author was matched, so the position is known.

fields
  • math.OC19
  • eess.SY3
  • cs.CV1
  • cs.GT1
  • cs.LG1
same name
  • Peng Yi — 5 papers, h 3
  • Peng Yi — 4 papers, h 23
  • Peng Yi — 4 papers, h 4
  • Peng Yi — 4 papers, h 4
  • Peng Yi — 3 papers, h 3
  • Peng Yi — 3 papers, h 22

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152022
most citedInitialization-free Distributed Algorithms for Optimal Resource Allocation with Feasibility Constraints and its Application to Economic Dispatch of Power Systems

489 citations · 1.2k across the 25 of their papers we have counts for

collaborators
Showing 2017 · math.OCShow all

3 papers · 2 filters

math.OC2017★ 82 cited

Distributed generalized Nash equilibria computation of monotone games via a preconditioned proximal point algorithm

Peng Yi, Lacra Pavel

In this paper, we investigate distributed generalized Nash equilibrium (GNE) computation of monotone games with affine coupling constraints. Each player can only utilize its local…

math.OC2017★ 281 cited

A distributed primal-dual algorithm for computation of generalized Nash equilibria with shared affine coupling constraints via operator splitting methods

Peng Yi, Lacra Pavel

In this paper, we propose a distributed primal-dual algorithm for computation of a generalized Nash equilibrium (GNE) in noncooperative games over network systems. In the considere…

math.OC2017

Projected Primal-Dual Gradient Flow of Augmented Lagrangian with Application to Distributed Maximization of the Algebraic Connectivity of a Network

Han Zhang, Jieqiang Wei, Peng Yi +1

In this paper, a projected primal-dual gradient flow of augmented Lagrangian is presented to solve convex optimization problems that are not necessarily strictly convex. The optimi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.