3,830 research outputs found
Adding Isolated Vertices Makes some Online Algorithms Optimal
An unexpected difference between online and offline algorithms is observed.
The natural greedy algorithms are shown to be worst case online optimal for
Online Independent Set and Online Vertex Cover on graphs with 'enough' isolated
vertices, Freckle Graphs. For Online Dominating Set, the greedy algorithm is
shown to be worst case online optimal on graphs with at least one isolated
vertex. These algorithms are not online optimal in general. The online
optimality results for these greedy algorithms imply optimality according to
various worst case performance measures, such as the competitive ratio. It is
also shown that, despite this worst case optimality, there are Freckle graphs
where the greedy independent set algorithm is objectively less good than
another algorithm. It is shown that it is NP-hard to determine any of the
following for a given graph: the online independence number, the online vertex
cover number, and the online domination number.Comment: A footnote in the .tex file didn't show up in the last version. This
was fixe
Two Optimal Strategies for Active Learning of Causal Models from Interventional Data
From observational data alone, a causal DAG is only identifiable up to Markov
equivalence. Interventional data generally improves identifiability; however,
the gain of an intervention strongly depends on the intervention target, that
is, the intervened variables. We present active learning (that is, optimal
experimental design) strategies calculating optimal interventions for two
different learning goals. The first one is a greedy approach using
single-vertex interventions that maximizes the number of edges that can be
oriented after each intervention. The second one yields in polynomial time a
minimum set of targets of arbitrary size that guarantees full identifiability.
This second approach proves a conjecture of Eberhardt (2008) indicating the
number of unbounded intervention targets which is sufficient and in the worst
case necessary for full identifiability. In a simulation study, we compare our
two active learning approaches to random interventions and an existing
approach, and analyze the influence of estimation errors on the overall
performance of active learning
Layout Decomposition for Quadruple Patterning Lithography and Beyond
For next-generation technology nodes, multiple patterning lithography (MPL)
has emerged as a key solution, e.g., triple patterning lithography (TPL) for
14/11nm, and quadruple patterning lithography (QPL) for sub-10nm. In this
paper, we propose a generic and robust layout decomposition framework for QPL,
which can be further extended to handle any general K-patterning lithography
(K4). Our framework is based on the semidefinite programming (SDP)
formulation with novel coloring encoding. Meanwhile, we propose fast yet
effective coloring assignment and achieve significant speedup. To our best
knowledge, this is the first work on the general multiple patterning
lithography layout decomposition.Comment: DAC'201
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