30,967 research outputs found
Parameterized Algorithms for Modular-Width
It is known that a number of natural graph problems which are FPT
parameterized by treewidth become W-hard when parameterized by clique-width. It
is therefore desirable to find a different structural graph parameter which is
as general as possible, covers dense graphs but does not incur such a heavy
algorithmic penalty.
The main contribution of this paper is to consider a parameter called
modular-width, defined using the well-known notion of modular decompositions.
Using a combination of ILPs and dynamic programming we manage to design FPT
algorithms for Coloring and Partitioning into paths (and hence Hamiltonian path
and Hamiltonian cycle), which are W-hard for both clique-width and its recently
introduced restriction, shrub-depth. We thus argue that modular-width occupies
a sweet spot as a graph parameter, generalizing several simpler notions on
dense graphs but still evading the "price of generality" paid by clique-width.Comment: to appear in IPEC 2013. arXiv admin note: text overlap with
arXiv:1304.5479 by other author
Lazy Evaluation and Delimited Control
The call-by-need lambda calculus provides an equational framework for
reasoning syntactically about lazy evaluation. This paper examines its
operational characteristics. By a series of reasoning steps, we systematically
unpack the standard-order reduction relation of the calculus and discover a
novel abstract machine definition which, like the calculus, goes "under
lambdas." We prove that machine evaluation is equivalent to standard-order
evaluation. Unlike traditional abstract machines, delimited control plays a
significant role in the machine's behavior. In particular, the machine replaces
the manipulation of a heap using store-based effects with disciplined
management of the evaluation stack using control-based effects. In short, state
is replaced with control. To further articulate this observation, we present a
simulation of call-by-need in a call-by-value language using delimited control
operations
Distances for Weighted Transition Systems: Games and Properties
We develop a general framework for reasoning about distances between
transition systems with quantitative information. Taking as starting point an
arbitrary distance on system traces, we show how this leads to natural
definitions of a linear and a branching distance on states of such a transition
system. We show that our framework generalizes and unifies a large variety of
previously considered system distances, and we develop some general properties
of our distances. We also show that if the trace distance admits a recursive
characterization, then the corresponding branching distance can be obtained as
a least fixed point to a similar recursive characterization. The central tool
in our work is a theory of infinite path-building games with quantitative
objectives.Comment: In Proceedings QAPL 2011, arXiv:1107.074
Mean-Variance Optimization in Markov Decision Processes
We consider finite horizon Markov decision processes under performance
measures that involve both the mean and the variance of the cumulative reward.
We show that either randomized or history-based policies can improve
performance. We prove that the complexity of computing a policy that maximizes
the mean reward under a variance constraint is NP-hard for some cases, and
strongly NP-hard for others. We finally offer pseudopolynomial exact and
approximation algorithms.Comment: A full version of an ICML 2011 pape
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