219 research outputs found
Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition
In this paper we consider a novel partitioned framework for distributed
optimization in peer-to-peer networks. In several important applications the
agents of a network have to solve an optimization problem with two key
features: (i) the dimension of the decision variable depends on the network
size, and (ii) cost function and constraints have a sparsity structure related
to the communication graph. For this class of problems a straightforward
application of existing consensus methods would show two inefficiencies: poor
scalability and redundancy of shared information. We propose an asynchronous
distributed algorithm, based on dual decomposition and coordinate methods, to
solve partitioned optimization problems. We show that, by exploiting the
problem structure, the solution can be partitioned among the nodes, so that
each node just stores a local copy of a portion of the decision variable
(rather than a copy of the entire decision vector) and solves a small-scale
local problem
Optimal strategies in the average consensus problem
We prove that for a set of communicating agents to compute the average of
their initial positions (average consensus problem), the optimal topology of
communication is given by a de Bruijn's graph. Consensus is then reached in a
finitely many steps. A more general family of strategies, constructed by block
Kronecker products, is investigated and compared to Cayley strategies.Comment: 9 pages; extended preprint with proofs of a CDC 2007 (Conference on
decision and Control) pape
Task Release Control for Decision Making Queues
We consider the optimal duration allocation in a decision making queue.
Decision making tasks arrive at a given rate to a human operator. The
correctness of the decision made by human evolves as a sigmoidal function of
the duration allocated to the task. Each task in the queue loses its value
continuously. We elucidate on this trade-off and determine optimal policies for
the human operator. We show the optimal policy requires the human to drop some
tasks. We present a receding horizon optimization strategy, and compare it with
the greedy policy.Comment: 8 pages, Submitted to American Controls Conference, San Francisco,
CA, June 201
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