4,475,988 research outputs found
A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks
We study the problem of scheduling sensors in a resource-constrained linear
dynamical system, where the objective is to select a small subset of sensors
from a large network to perform the state estimation task. We formulate this
problem as the maximization of a monotone set function under a matroid
constraint. We propose a randomized greedy algorithm that is significantly
faster than state-of-the-art methods. By introducing the notion of curvature
which quantifies how close a function is to being submodular, we analyze the
performance of the proposed algorithm and find a bound on the expected mean
square error (MSE) of the estimator that uses the selected sensors in terms of
the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for
the scenario where{\color{black}{ the measurements are i.i.d. random vectors
with bounded norm.}} Simulation results demonstrate efficacy of the
randomized greedy algorithm in a comparison with greedy and semidefinite
programming relaxation methods
A Connectedness Constraint for Learning Sparse Graphs
Graphs are naturally sparse objects that are used to study many problems
involving networks, for example, distributed learning and graph signal
processing. In some cases, the graph is not given, but must be learned from the
problem and available data. Often it is desirable to learn sparse graphs.
However, making a graph highly sparse can split the graph into several
disconnected components, leading to several separate networks. The main
difficulty is that connectedness is often treated as a combinatorial property,
making it hard to enforce in e.g. convex optimization problems. In this
article, we show how connectedness of undirected graphs can be formulated as an
analytical property and can be enforced as a convex constraint. We especially
show how the constraint relates to the distributed consensus problem and graph
Laplacian learning. Using simulated and real data, we perform experiments to
learn sparse and connected graphs from data.Comment: 5 pages, presented at the European Signal Processing Conference
(EUSIPCO) 201
A Lagrangian Policy for Optimal Energy Storage Control
This paper presents a millisecond-level look-ahead control algorithm for
energy storage with constant space complexity and worst-case linear run-time
complexity. The algorithm connects the optimal control with the Lagrangian
multiplier associated with the state-of-charge constraint. It is compared to
solving look-ahead control using a state-of-the-art convex optimization solver.
Simulation results show that both methods obtain the same control result, while
the proposed algorithm runs up to 100,000 times faster and solves most problems
within one millisecond. The theoretical results from developing this algorithm
also provide key insights into designing optimal energy storage control schemes
at the centralized system level as well as under distributed settings
School Effectiveness Grant and Pupil Deprivation Grant 2012–2013
"This is a guidance document for schools, local authorities and education consortia in Wales on the School Effectiveness Grant and Pupil Deprivation Grant. It includes priorities for expenditure in 2012–2013, grant allocations and arrangements for claiming the grant" - inside front cover
Pilot Planning Grant
Report summarizing key findings of focus groups assessing Georgians' attitudes and opinions regarding the development of a plan for providing affordable insurance coverage statewide
Grant Helps Broaden Perspectives
Fulbright winner Alexis Lien ’05 is spending the 2005-2006 academic year teaching, studying and conducting research in Austria
Davis Digital Repository Grant
The HELIN (Higher Education Library Information Network) Consortium, consisting of academic and health sciences libraries in Rhode Island and Massachusetts, has developed a plan to create a digital repository to archive, preserve and make accessible materials to serve the needs of its students and faculty. This grant application outlines the project purpose, methods, costs, and timetable
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