321,717 research outputs found
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
Many problems in machine learning and other fields can be (re)for-mulated as
linearly constrained separable convex programs. In most of the cases, there are
multiple blocks of variables. However, the traditional alternating direction
method (ADM) and its linearized version (LADM, obtained by linearizing the
quadratic penalty term) are for the two-block case and cannot be naively
generalized to solve the multi-block case. So there is great demand on
extending the ADM based methods for the multi-block case. In this paper, we
propose LADM with parallel splitting and adaptive penalty (LADMPSAP) to solve
multi-block separable convex programs efficiently. When all the component
objective functions have bounded subgradients, we obtain convergence results
that are stronger than those of ADM and LADM, e.g., allowing the penalty
parameter to be unbounded and proving the sufficient and necessary conditions}
for global convergence. We further propose a simple optimality measure and
reveal the convergence rate of LADMPSAP in an ergodic sense. For programs with
extra convex set constraints, with refined parameter estimation we devise a
practical version of LADMPSAP for faster convergence. Finally, we generalize
LADMPSAP to handle programs with more difficult objective functions by
linearizing part of the objective function as well. LADMPSAP is particularly
suitable for sparse representation and low-rank recovery problems because its
subproblems have closed form solutions and the sparsity and low-rankness of the
iterates can be preserved during the iteration. It is also highly
parallelizable and hence fits for parallel or distributed computing. Numerical
experiments testify to the advantages of LADMPSAP in speed and numerical
accuracy.Comment: Preliminary version published on Asian Conference on Machine Learning
201
An optimal variance estimate in stochastic homogenization of discrete elliptic equations
We consider a discrete elliptic equation on the -dimensional lattice
with random coefficients of the simplest type: they are
identically distributed and independent from edge to edge. On scales large
w.r.t. the lattice spacing (i.e., unity), the solution operator is known to
behave like the solution operator of a (continuous) elliptic equation with
constant deterministic coefficients. This symmetric ``homogenized'' matrix
is characterized by
for any direction , where the
random field (the ``corrector'') is the unique solution of
such that , is
stationary and , denoting the
ensemble average (or expectation). It is known (``by ergodicity'') that the
above ensemble average of the energy density , which is a stationary random
field, can be recovered by a system average. We quantify this by proving that
the variance of a spatial average of on length scales
satisfies the optimal estimate, that is, , where the averaging function [i.e., ,
] has to be smooth in the
sense that . In two space dimensions (i.e.,
), there is a logarithmic correction. This estimate is optimal since it
shows that smooth averages of the energy density decay in as
if would be independent from edge to edge (which it is not for
). This result is of practical significance, since it allows to estimate
the dominant error when numerically computing .Comment: Published in at http://dx.doi.org/10.1214/10-AOP571 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Deaf, Dumb, and Chatting Robots, Enabling Distributed Computation and Fault-Tolerance Among Stigmergic Robot
We investigate ways for the exchange of information (explicit communication)
among deaf and dumb mobile robots scattered in the plane. We introduce the use
of movement-signals (analogously to flight signals and bees waggle) as a mean
to transfer messages, enabling the use of distributed algorithms among the
robots. We propose one-to-one deterministic movement protocols that implement
explicit communication. We first present protocols for synchronous robots. We
begin with a very simple coding protocol for two robots. Based on on this
protocol, we provide one-to-one communication for any system of n \geq 2 robots
equipped with observable IDs that agree on a common direction (sense of
direction). We then propose two solutions enabling one-to-one communication
among anonymous robots. Since the robots are devoid of observable IDs, both
protocols build recognition mechanisms using the (weak) capabilities offered to
the robots. The first protocol assumes that the robots agree on a common
direction and a common handedness (chirality), while the second protocol
assumes chirality only. Next, we show how the movements of robots can provide
implicit acknowledgments in asynchronous systems. We use this result to design
asynchronous one-to-one communication with two robots only. Finally, we combine
this solution with the schemes developed in synchronous settings to fit the
general case of asynchronous one-to-one communication among any number of
robots. Our protocols enable the use of distributing algorithms based on
message exchanges among swarms of Stigmergic robots. Furthermore, they provides
robots equipped with means of communication to overcome faults of their
communication device
Designing the Sakai Open Academic Environment: A distributed cognition account of the design of a large scale software system
Social accounts of technological change make the flexibility and openness of interpretations the starting point of an argument against technological determinism. They suggest that technological change unfolds in the semantic domain, but they focus on the social processes around the interpretations of new technologies, and do not address the conceptual processes of change in interpretations. The dissertation presents an empirically grounded case study of the design process of an open-source online software platform based on the framework of distributed cognition to argue that the cognitive perspective is needed for understanding innovation in software, because it allows us to describe the reflexive and expansive contribution of conceptual processes to new software and the significance of professional epistemic practices in framing the direction of innovation. The framework of distributed cognition brings the social and cognitive perspectives together on account of its understanding of conceptual processes as distributed over time, among people, and between humans and artifacts. The dissertation argues that an evolving open-source software landscape became translated into the open-ended local design space of a new software project in a process of infrastructural implosion, and the design space prompted participants to outline and pursue epistemic strategies of sense-making and learning about the contexts of use. The result was a process of conceptual modeling, which resulted in a conceptually novel user interface. Prototyping professional practices of user-centered design lent directionality to this conceptual process in terms of a focus on individual activities with the user interface. Social approaches to software design under the broad umbrella of human-centered computing have been seeking to inform the design on the basis of empirical contributions about a social context. The analysis has shown that empirical engagement with the contexts of use followed from conceptual modeling, and concern about real world contexts was aligned with the user-centered direction that design was taking. I also point out a social-technical gap in the design process in connection with the repeated performance challenges that the platform was facing, and describe the possibility of a social-technical imagination.Ph.D
Minimal chordal sense of direction and circulant graphs
A sense of direction is an edge labeling on graphs that follows a globally
consistent scheme and is known to considerably reduce the complexity of several
distributed problems. In this paper, we study a particular instance of sense of
direction, called a chordal sense of direction (CSD). In special, we identify
the class of k-regular graphs that admit a CSD with exactly k labels (a minimal
CSD). We prove that connected graphs in this class are Hamiltonian and that the
class is equivalent to that of circulant graphs, presenting an efficient
(polynomial-time) way of recognizing it when the graphs' degree k is fixed
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
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