  Fast and accurate unveiling of power line outages is of paramount importance
not only for preventing faults that may lead to blackouts, but also for routine
monitoring and control tasks of the smart grid, including state estimation and
optimal power flow. Existing approaches are either challenged by the
\emph{combinatorial complexity} issues involved, and are thus limited to
identifying single- and double-line outages; or, they invoke less pragmatic
assumptions such as \emph{conditionally independent} phasor angle measurements
available across the grid. Using only a subset of voltage phasor angle data,
the present paper develops a near real-time algorithm for identifying multiple
line outages at the affordable complexity of solving a quadratic program via
block coordinate descent iterations. The novel approach relies on reformulating
the DC linear power flow model as a \emph{sparse} overcomplete expansion, and
leveraging contemporary advances in compressive sampling and variable selection
using the least-absolute shrinkage and selection operator (Lasso). Analysis and
simulated tests on the standard IEEE 118-bus system confirm the effectiveness
of lassoing line changes in the smart power grid.
