4,253 research outputs found
Sparse Stabilization and Control of Alignment Models
From a mathematical point of view self-organization can be described as
patterns to which certain dynamical systems modeling social dynamics tend
spontaneously to be attracted. In this paper we explore situations beyond
self-organization, in particular how to externally control such dynamical
systems in order to eventually enforce pattern formation also in those
situations where this wished phenomenon does not result from spontaneous
convergence. Our focus is on dynamical systems of Cucker-Smale type, modeling
consensus emergence, and we question the existence of stabilization and optimal
control strategies which require the minimal amount of external intervention
for nevertheless inducing consensus in a group of interacting agents. We
provide a variational criterion to explicitly design feedback controls that are
componentwise sparse, i.e. with at most one nonzero component at every instant
of time. Controls sharing this sparsity feature are very realistic and
convenient for practical issues. Moreover, the maximally sparse ones are
instantaneously optimal in terms of the decay rate of a suitably designed
Lyapunov functional, measuring the distance from consensus. As a consequence we
provide a mathematical justification to the general principle according to
which "sparse is better" in the sense that a policy maker, who is not allowed
to predict future developments, should always consider more favorable to
intervene with stronger action on the fewest possible instantaneous optimal
leaders rather than trying to control more agents with minor strength in order
to achieve group consensus. We then establish local and global sparse
controllability properties to consensus and, finally, we analyze the sparsity
of solutions of the finite time optimal control problem where the minimization
criterion is a combination of the distance from consensus and of the l1-norm of
the control.Comment: 33 pages, 5 figure
Learning parametric dictionaries for graph signals
In sparse signal representation, the choice of a dictionary often involves a
tradeoff between two desirable properties -- the ability to adapt to specific
signal data and a fast implementation of the dictionary. To sparsely represent
signals residing on weighted graphs, an additional design challenge is to
incorporate the intrinsic geometric structure of the irregular data domain into
the atoms of the dictionary. In this work, we propose a parametric dictionary
learning algorithm to design data-adapted, structured dictionaries that
sparsely represent graph signals. In particular, we model graph signals as
combinations of overlapping local patterns. We impose the constraint that each
dictionary is a concatenation of subdictionaries, with each subdictionary being
a polynomial of the graph Laplacian matrix, representing a single pattern
translated to different areas of the graph. The learning algorithm adapts the
patterns to a training set of graph signals. Experimental results on both
synthetic and real datasets demonstrate that the dictionaries learned by the
proposed algorithm are competitive with and often better than unstructured
dictionaries learned by state-of-the-art numerical learning algorithms in terms
of sparse approximation of graph signals. In contrast to the unstructured
dictionaries, however, the dictionaries learned by the proposed algorithm
feature localized atoms and can be implemented in a computationally efficient
manner in signal processing tasks such as compression, denoising, and
classification
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