14,263 research outputs found
Finding the K shortest hyperpaths using reoptimization
The shortest hyperpath problem is an extension of the classical shortest path problem and has applications in many different areas. Recently, algorithms for finding the K shortest hyperpaths in a directed hypergraph have been developed by Andersen, Nielsen and Pretolani. In this paper we improve the worst-case computational complexity of an algorithm for finding the K shortest hyperpaths in an acyclic hypergraph. This result is obtained by applying new reoptimization techniques for shortest hyperpaths. The algorithm turns out to be quite effective in practice and has already been successfully applied in the context of stochastic time-dependent networks, for finding the K best strategies and for solving bicriterion problems.Network programming; Directed hypergraphs; K shortest hyperpaths; K shortest paths
K shortest paths in stochastic time-dependent networks
A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. In some particular cases, the shortest origin-destination path must nevertheless be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is NP-hard, while the best time-adaptive strategy can be found in polynomial time. In this paper, we propose a solution method for the a priori shortest path problem, and we show that it can be easily adapted to the ranking of the first K shortest paths. Moreover, we present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effectiveShortest paths; K shortest paths; stochastic time-dependent networks; routing; directed hypergraphs
A note on âMulticriteria adaptive paths in stochastic, time-varying networksâ
In a recent paper, Opasanon and Miller-Hooks study multicriteria adaptive paths in stochastic time-varying networks. They propose a label correcting algorithm for finding the full set of efficient strategies. In this note we show that their algorithm is not correct, since it is based on a property that does not hold in general. Opasanon and Miller-Hooks also propose an algorithm for solving a parametric problem. We give a simplified algorithm which is linear in the input size.Multiple objective programming; shortest paths; stochastic time-dependent networks; time-adaptive strategies
Bicriterion a priori route choice in stochastic time-dependent networks.
In recent years there has been a growing interest in using stochastic time-dependent (STD) networks as a modelling tool for a number of applications within such areas as transportation and telecommunications. It is known that an optimal routing policy does not necessarily correspond to a path, but rather to a time-adaptive strategy. In some applications, however, it makes good sense to require that the routing policy corresponds to a loopless path in the network, that is, the time-adaptive aspect disappears and a priori route choice is considered. In this paper we consider bicriterion a priori route choice in STD networks, i.e. the problem of finding the set of efficient paths. Both expectation and min-max criteria are considered and a solution method based on the two-phase approach is devised. Experimental results reveal that the full set of efficient solutions can be determined on rather large test instances, which is in contrast to previously reported results for the time-adaptive caseStochastic time-dependent networks; Bicriterion shortest path; A priori route choice; Two-phase method
Using superlattice potentials to probe long-range magnetic correlations in optical lattices
In Pedersen et al. (2011) we proposed a method to utilize a temporally
dependent superlattice potential to mediate spin-selective transport, and
thereby probe long and short range magnetic correlations in optical lattices.
Specifically this can be used for detecting antiferromagnetic ordering in
repulsive fermionic optical lattice systems, but more generally it can serve as
a means of directly probing correlations among the atoms by measuring the mean
value of an observable, the number of double occupied sites. Here, we provide a
detailed investigation of the physical processes which limit the effectiveness
of this "conveyer belt method". Furthermore we propose a simple ways to improve
the procedure, resulting in an essentially perfect (error-free) probing of the
magnetic correlations. These results shows that suitably constructed
superlattices constitute a promising way of manipulating atoms of different
spin species as well as probing their interactions.Comment: 12 pages, 9 figure
Recommended from our members
Quantile forecasts of daily exchange rate returns from forecasts of realized volatility
Quantile forecasts are central to risk management decisions because of the widespread
use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to
forecast volatility, and the method of computing quantiles from the volatility forecasts. In
this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns
of five currencies. The forecasting models that have been used in recent analyses of the
predictability of daily realized volatility permit a comparison of the predictive power of
different measures of intraday variation and intraday returns in forecasting exchange rate
variability. The methods of computing quantile forecasts include making distributional
assumptions for future daily returns as well as using the empirical distribution of predicted
standardized returns with both rolling and recursive samples. Our main findings are that the
Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts
for currencies which experience shifts in volatility, such as the Canadian dollar, and that
the use of the empirical distribution to calculate quantiles can improve forecasts when there
are shifts
- âŚ