A. Ozdaglar

Publications352
h-index64
Citations21,790
Highly Influential Citations2,172
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Distributed Subgradient Methods for Multi-Agent Optimization
  • A. Nedić, A. Ozdaglar
  • Mathematics, Computer Science
    IEEE Transactions on Automatic Control
  • 13 January 2009
TLDR
The authors' convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.
The Network Origins of Aggregate Fluctuations
This paper argues that in the presence of intersectoral input-output linkages, microeconomic idiosyncratic shocks may lead to aggregate fluctuations. In particular, it shows that, as the economy…
Systemic Risk and Stability in Financial Networks
This paper argues that the extent of financial contagion exhibits a form of phase transition: as long as the magnitude of negative shocks affecting financial institutions are sufficiently small, a…
Constrained Consensus and Optimization in Multi-Agent Networks
TLDR
A distributed "projected subgradient algorithm" which involves each agent performing a local averaging operation, taking a subgradient step to minimize its own objective function, and projecting on its constraint set, and it is shown that, with an appropriately selected stepsize rule, the agent estimates generated by this algorithm converge to the same optimal solution.
Convex Analysis and Optimization
Bayesian Learning in Social Networks
TLDR
The main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning.
Opinion Dynamics and Learning in Social Networks
TLDR
An overview of recent research on belief and opinion dynamics in social networks is provided and the implications of the form of learning, sources of information, and the structure of social networks are discussed.
Distributed Alternating Direction Method of Multipliers
  • Ermin Wei, A. Ozdaglar
  • Computer Science, Mathematics
    IEEE 51st IEEE Conference on Decision and Control…
  • 1 December 2012
TLDR
This paper introduces a new distributed optimization algorithm based on Alternating Direction Method of Multipliers (ADMM), which is a classical method for sequentially decomposing optimization problems with coupled constraints and shows that this algorithm converges at the rate O (1/k).
The Network Origins of Aggregate Fluctuations
This paper argues that in the presence of intersectoral input-output linkages, microeconomic idiosyncratic shocks may lead to aggregate fluctuations. In particular, it shows that, as the economy…
Subgradient Methods for Saddle-Point Problems
  • A. Nedić, A. Ozdaglar
  • Computer Science, Mathematics
    J. Optimization Theory and Applications
  • 5 March 2009
TLDR
This work presents a subgradient algorithm for generating approximate saddle points and provides per-iteration convergence rate estimates on the constructed solutions, and focuses on Lagrangian duality, where it is shown this algorithm is particularly well-suited for problems where the subgradient of the dual function cannot be evaluated easily.
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