81 research outputs found
Information Structure Design in Team Decision Problems
We consider a problem of information structure design in team decision
problems and team games. We propose simple, scalable greedy algorithms for
adding a set of extra information links to optimize team performance and
resilience to non-cooperative and adversarial agents. We show via a simple
counterexample that the set function mapping additional information links to
team performance is in general not supermodular. Although this implies that the
greedy algorithm is not accompanied by worst-case performance guarantees, we
illustrate through numerical experiments that it can produce effective and
often optimal or near optimal information structure modifications
On Submodularity and Controllability in Complex Dynamical Networks
Controllability and observability have long been recognized as fundamental
structural properties of dynamical systems, but have recently seen renewed
interest in the context of large, complex networks of dynamical systems. A
basic problem is sensor and actuator placement: choose a subset from a finite
set of possible placements to optimize some real-valued controllability and
observability metrics of the network. Surprisingly little is known about the
structure of such combinatorial optimization problems. In this paper, we show
that several important classes of metrics based on the controllability and
observability Gramians have a strong structural property that allows for either
efficient global optimization or an approximation guarantee by using a simple
greedy heuristic for their maximization. In particular, the mapping from
possible placements to several scalar functions of the associated Gramian is
either a modular or submodular set function. The results are illustrated on
randomly generated systems and on a problem of power electronic actuator
placement in a model of the European power grid.Comment: Original arXiv version of IEEE Transactions on Control of Network
Systems paper (Volume 3, Issue 1), with a addendum (located in the ancillary
documents) that explains an error in a proof of the original paper and
provides a counterexample to the corresponding resul
Minimal Reachability is Hard To Approximate
In this note, we consider the problem of choosing which nodes of a linear
dynamical system should be actuated so that the state transfer from the
system's initial condition to a given final state is possible. Assuming a
standard complexity hypothesis, we show that this problem cannot be efficiently
solved or approximated in polynomial, or even quasi-polynomial, time
Minimal Actuator Placement with Optimal Control Constraints
We introduce the problem of minimal actuator placement in a linear control
system so that a bound on the minimum control effort for a given state transfer
is satisfied while controllability is ensured. We first show that this is an
NP-hard problem following the recent work of Olshevsky. Next, we prove that
this problem has a supermodular structure. Afterwards, we provide an efficient
algorithm that approximates up to a multiplicative factor of O(logn), where n
is the size of the multi-agent network, any optimal actuator set that meets the
specified energy criterion. Moreover, we show that this is the best
approximation factor one can achieve in polynomial-time for the worst case.
Finally, we test this algorithm over large Erdos-Renyi random networks to
further demonstrate its efficiency.Comment: This version includes all the omitted proofs from the one to appear
in the American Control Conference (ACC) 2015 proceeding
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