25,684 research outputs found
Cloud-Based Optimization: A Quasi-Decentralized Approach to Multi-Agent Coordination
New architectures and algorithms are needed to reflect the mixture of local
and global information that is available as multi-agent systems connect over
the cloud. We present a novel architecture for multi-agent coordination where
the cloud is assumed to be able to gather information from all agents, perform
centralized computations, and disseminate the results in an intermittent
manner. This architecture is used to solve a multi-agent optimization problem
in which each agent has a local objective function unknown to the other agents
and in which the agents are collectively subject to global inequality
constraints. Leveraging the cloud, a dual problem is formulated and solved by
finding a saddle point of the associated Lagrangian.Comment: 7 pages, 3 figure
Exploiting Chordality in Optimization Algorithms for Model Predictive Control
In this chapter we show that chordal structure can be used to devise
efficient optimization methods for many common model predictive control
problems. The chordal structure is used both for computing search directions
efficiently as well as for distributing all the other computations in an
interior-point method for solving the problem. The chordal structure can stem
both from the sequential nature of the problem as well as from distributed
formulations of the problem related to scenario trees or other formulations.
The framework enables efficient parallel computations.Comment: arXiv admin note: text overlap with arXiv:1502.0638
Optimal Reconfiguration of Formation Flying Spacecraft--a Decentralized Approach
This paper introduces a hierarchical, decentralized,
and parallelizable method for dealing with optimization
problems with many agents. It is theoretically based on a hierarchical
optimization theorem that establishes the equivalence
of two forms of the problem, and this idea is implemented using
DMOC (Discrete Mechanics and Optimal Control). The result
is a method that is scalable to certain optimization problems
for large numbers of agents, whereas the usual “monolithic”
approach can only deal with systems with a rather small
number of degrees of freedom. The method is illustrated with
the example of deployment of spacecraft, motivated by the
Darwin (ESA) and Terrestrial Planet Finder (NASA) missions
A Finite-Time Cutting Plane Algorithm for Distributed Mixed Integer Linear Programming
Many problems of interest for cyber-physical network systems can be
formulated as Mixed Integer Linear Programs in which the constraints are
distributed among the agents. In this paper we propose a distributed algorithm
to solve this class of optimization problems in a peer-to-peer network with no
coordinator and with limited computation and communication capabilities. In the
proposed algorithm, at each communication round, agents solve locally a small
LP, generate suitable cutting planes, namely intersection cuts and cost-based
cuts, and communicate a fixed number of active constraints, i.e., a candidate
optimal basis. We prove that, if the cost is integer, the algorithm converges
to the lexicographically minimal optimal solution in a finite number of
communication rounds. Finally, through numerical computations, we analyze the
algorithm convergence as a function of the network size.Comment: 6 pages, 3 figure
A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
In this paper we deal with a network of agents seeking to solve in a
distributed way Mixed-Integer Linear Programs (MILPs) with a coupling
constraint (modeling a limited shared resource) and local constraints. MILPs
are NP-hard problems and several challenges arise in a distributed framework,
so that looking for suboptimal solutions is of interest. To achieve this goal,
the presence of a linear coupling calls for tailored decomposition approaches.
We propose a fully distributed algorithm based on a primal decomposition
approach and a suitable tightening of the coupling constraints. Agents
repeatedly update local allocation vectors, which converge to an optimal
resource allocation of an approximate version of the original problem. Based on
such allocation vectors, agents are able to (locally) compute a mixed-integer
solution, which is guaranteed to be feasible after a sufficiently large time.
Asymptotic and finite-time suboptimality bounds are established for the
computed solution. Numerical simulations highlight the efficacy of the proposed
methodology.Comment: 57th IEEE Conference on Decision and Contro
A Mobile Computing Architecture for Numerical Simulation
The domain of numerical simulation is a place where the parallelization of
numerical code is common. The definition of a numerical context means the
configuration of resources such as memory, processor load and communication
graph, with an evolving feature: the resources availability. A feature is often
missing: the adaptability. It is not predictable and the adaptable aspect is
essential. Without calling into question these implementations of these codes,
we create an adaptive use of these implementations. Because the execution has
to be driven by the availability of main resources, the components of a numeric
computation have to react when their context changes. This paper offers a new
architecture, a mobile computing architecture, based on mobile agents and
JavaSpace. At the end of this paper, we apply our architecture to several case
studies and obtain our first results
Rational physical agent reasoning beyond logic
The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation
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