5,926 research outputs found
Online Permutation Routing in Partitioned Optical Passive Star Networks
This paper establishes the state of the art in both deterministic and
randomized online permutation routing in the POPS network. Indeed, we show that
any permutation can be routed online on a POPS network either with
deterministic slots, or, with high probability, with
randomized slots, where constant
. When , that we claim to be the
"interesting" case, the randomized algorithm is exponentially faster than any
other algorithm in the literature, both deterministic and randomized ones. This
is true in practice as well. Indeed, experiments show that it outperforms its
rivals even starting from as small a network as a POPS(2,2), and the gap grows
exponentially with the size of the network. We can also show that, under proper
hypothesis, no deterministic algorithm can asymptotically match its
performance
Checking Dynamic Consistency of Conditional Hyper Temporal Networks via Mean Payoff Games (Hardness and (pseudo) Singly-Exponential Time Algorithm)
In this work we introduce the \emph{Conditional Hyper Temporal Network
(CHyTN)} model, which is a natural extension and generalization of both the
\CSTN and the \HTN model. Our contribution goes as follows. We show that
deciding whether a given \CSTN or CHyTN is dynamically consistent is
\coNP-hard. Then, we offer a proof that deciding whether a given CHyTN is
dynamically consistent is \PSPACE-hard, provided that the input instances are
allowed to include both multi-head and multi-tail hyperarcs. In light of this,
we continue our study by focusing on CHyTNs that allow only multi-head or only
multi-tail hyperarcs, and we offer the first deterministic (pseudo)
singly-exponential time algorithm for the problem of checking the
dynamic-consistency of such CHyTNs, also producing a dynamic execution strategy
whenever the input CHyTN is dynamically consistent. Since \CSTN{s} are a
special case of CHyTNs, this provides as a byproduct the first
sound-and-complete (pseudo) singly-exponential time algorithm for checking
dynamic-consistency in CSTNs. The proposed algorithm is based on a novel
connection between CSTN{s}/CHyTN{s} and Mean Payoff Games. The presentation of
the connection between \CSTN{s}/CHyTNs and \MPG{s} is mediated by the \HTN
model. In order to analyze the algorithm, we introduce a refined notion of
dynamic-consistency, named -dynamic-consistency, and present a sharp
lower bounding analysis on the critical value of the reaction time
where a \CSTN/CHyTN transits from being, to not being,
dynamically consistent. The proof technique introduced in this analysis of
is applicable more generally when dealing with linear
difference constraints which include strict inequalities.Comment: arXiv admin note: text overlap with arXiv:1505.0082
Some results on more flexible versions of Graph Motif
The problems studied in this paper originate from Graph Motif, a problem
introduced in 2006 in the context of biological networks. Informally speaking,
it consists in deciding if a multiset of colors occurs in a connected subgraph
of a vertex-colored graph. Due to the high rate of noise in the biological
data, more flexible definitions of the problem have been outlined. We present
in this paper two inapproximability results for two different optimization
variants of Graph Motif: one where the size of the solution is maximized, the
other when the number of substitutions of colors to obtain the motif from the
solution is minimized. We also study a decision version of Graph Motif where
the connectivity constraint is replaced by the well known notion of graph
modularity. While the problem remains NP-complete, it allows algorithms in FPT
for biologically relevant parameterizations
Dynamic Consistency of Conditional Simple Temporal Networks via Mean Payoff Games: a Singly-Exponential Time DC-Checking
Conditional Simple Temporal Network (CSTN) is a constraint-based
graph-formalism for conditional temporal planning. It offers a more flexible
formalism than the equivalent CSTP model of Tsamardinos, Vidal and Pollack,
from which it was derived mainly as a sound formalization. Three notions of
consistency arise for CSTNs and CSTPs: weak, strong, and dynamic. Dynamic
consistency is the most interesting notion, but it is also the most challenging
and it was conjectured to be hard to assess. Tsamardinos, Vidal and Pollack
gave a doubly-exponential time algorithm for deciding whether a CSTN is
dynamically-consistent and to produce, in the positive case, a dynamic
execution strategy of exponential size. In the present work we offer a proof
that deciding whether a CSTN is dynamically-consistent is coNP-hard and provide
the first singly-exponential time algorithm for this problem, also producing a
dynamic execution strategy whenever the input CSTN is dynamically-consistent.
The algorithm is based on a novel connection with Mean Payoff Games, a family
of two-player combinatorial games on graphs well known for having applications
in model-checking and formal verification. The presentation of such connection
is mediated by the Hyper Temporal Network model, a tractable generalization of
Simple Temporal Networks whose consistency checking is equivalent to
determining Mean Payoff Games. In order to analyze the algorithm we introduce a
refined notion of dynamic-consistency, named \epsilon-dynamic-consistency, and
present a sharp lower bounding analysis on the critical value of the reaction
time \hat{\varepsilon} where the CSTN transits from being, to not being,
dynamically-consistent. The proof technique introduced in this analysis of
\hat{\varepsilon} is applicable more in general when dealing with linear
difference constraints which include strict inequalities
Routing Permutations in Partitioned Optical Passive Star Networks
It is shown that a POPS network with g groups and d processors per group can
efficiently route any permutation among the n=dg processors. The number of
slots used is optimal in the worst case, and is at most the double of the
optimum for all permutations p such that p(i)i for all i.Comment: 8 pages, 3 figure
Hybrid SAT-Based Consistency Checking Algorithms for Simple Temporal Networks with Decisions
A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN by adding decision time points to model temporal plans with decisions. A decision time point is a special kind of time point that once executed allows for deciding a truth value for an associated Boolean proposition. Furthermore, STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. Thus, an STND models a family of STNs each obtained as a projection of the initial STND onto a scenario. An STND is consistent if there exists a consistent scenario (i.e., a scenario such that the corresponding STN projection is consistent). Recently, a hybrid SAT-based consistency checking algorithm (HSCC) was proposed to check the consistency of an STND. Unfortunately, that approach lacks experimental evaluation and does not allow for the synthesis of all consistent scenarios. In this paper, we propose an incremental HSCC algorithm for STNDs that (i) is faster than the previous one and (ii) allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). Then, we carry out an experimental evaluation with KAPPA, a tool that we developed for STNDs. Finally, we prove that STNDs and disjunctive temporal networks (DTNs) are equivalent
Decoding Hidden Markov Models Faster Than Viterbi Via Online Matrix-Vector (max, +)-Multiplication
In this paper, we present a novel algorithm for the maximum a posteriori
decoding (MAPD) of time-homogeneous Hidden Markov Models (HMM), improving the
worst-case running time of the classical Viterbi algorithm by a logarithmic
factor. In our approach, we interpret the Viterbi algorithm as a repeated
computation of matrix-vector -multiplications. On time-homogeneous
HMMs, this computation is online: a matrix, known in advance, has to be
multiplied with several vectors revealed one at a time. Our main contribution
is an algorithm solving this version of matrix-vector -multiplication
in subquadratic time, by performing a polynomial preprocessing of the matrix.
Employing this fast multiplication algorithm, we solve the MAPD problem in
time for any time-homogeneous HMM of size and observation
sequence of length , with an extra polynomial preprocessing cost negligible
for . To the best of our knowledge, this is the first algorithm for the
MAPD problem requiring subquadratic time per observation, under the only
assumption -- usually verified in practice -- that the transition probability
matrix does not change with time.Comment: AAAI 2016, to appea
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