162,672 research outputs found
On-Line Difference Maximization
In this paper we examine problems motivated by on-line financial problems and
stochastic games. In particular, we consider a sequence of entirely arbitrary distinct values arriving
in random order, and must devise strategies for selecting low values followed by high values in such
a way as to maximize the expected gain in rank from low values to high values.
First, we consider a scenario in which only one low value and one high value may be selected.
We give an optimal on-line algorithm for this scenario, and analyze it to show that, surprisingly,
the expected gain is n - O(1), and so differs from the best possible off-line gain by only a constant
additive term (which is, in fact, fairly small|at most 15).
In a second scenario, we allow multiple nonoverlapping low/high selections, where the total gain
for our algorithm is the sum of the individual pair gains. We also give an optimal on-line algorithm
for this problem, where the expected gain is n2=8 - &#x0398(n log n). An analysis shows that the optimal
expected o-line gain is n2=6 + &#x0398(1), so the performance of our on-line algorithm is within a factor
of 3=4 of the best o-line strategy
An Experimental Comparison of the Fairness Models by Bolton and Ockenfels and by Fehr and Schmidt
We present an experiment to compare the two fairness theories by Bolton and Ockenfels [ERC] and by Fehr and Schmidt [F&S]. If one wants to compare their predictive power, most of the experiments that are interpreted retrospectively are not helpful, since both theories make equal or very similar predictions. Both models rely on inequality aversion. The fundamental difference between them is that ERC assumes that subjects like the average payoff to be as close as possible to their own payoff while F&S assumes that subjects dislike a payoff difference to any other individual. To obtain explicitly opposite predictions by the two theories we chose a game that focuses on their fundamental difference. A person received a fixed payoff and chose between three different allocations of money between a person who received in all allocations more than her and a person who always received less. The allocations with an average payoff for the other two persons closer to her's, had both individual payoffs more distant from her's. ERC predicts that she chooses the allocation that is most unequal between the other two persons. The choice of the opposite allocation is predicted by F&S. Subjects knew that their decision could never influence their own payoff. To prevent interference of preferences for efficiency with our objective, we designed two treatments, one where following the ERC prediction leads to a maximization of total payoff, one where maximization of total payoff is in line with the F&S prediction. In the second treatment the results clearly confirm the F&S prediction. In the first treatment subjects chose in about equal proportions the two extreme allocations. Hence the performance of F&S is much better than that of ERC, although both theories ignore the importance that subjects assign to efficiency.
Profit-oriented disassembly-line balancing
As product and material recovery has gained importance, disassembly volumes have increased, justifying construction of disassembly lines similar to assembly lines. Recent research on disassembly lines has focused on complete disassembly. Unlike assembly, the current industry practice involves partial disassembly with profit-maximization or cost-minimization objectives. Another difference between assembly and disassembly is that disassembly involves additional precedence relations among tasks due to processing alternatives or physical restrictions. In this study, we define and solve the profit-oriented partial disassembly-line balancing problem. We first characterize different types of precedence relations in disassembly and propose a new representation scheme that encompasses all these types. We then develop the first mixed integer programming formulation for the partial disassembly-line balancing problem, which simultaneously determines (1) the parts whose demand is to be fulfilled to generate revenue, (2) the tasks that will release the selected parts under task and station costs, (3) the number of stations that will be opened, (4) the cycle time, and (5) the balance of the disassembly line, i.e. the feasible assignment of selected tasks to stations such that various types of precedence relations are satisfied. We propose a lower and upper-bounding scheme based on linear programming relaxation of the formulation. Computational results show that our approach provides near optimal solutions for small problems and is capable of solving larger problems with up to 320 disassembly tasks in reasonable time
Best chirplet chain: near-optimal detection of gravitational wave chirps
The list of putative sources of gravitational waves possibly detected by the
ongoing worldwide network of large scale interferometers has been continuously
growing in the last years. For some of them, the detection is made difficult by
the lack of a complete information about the expected signal. We concentrate on
the case where the expected GW is a quasi-periodic frequency modulated signal
i.e., a chirp. In this article, we address the question of detecting an a
priori unknown GW chirp. We introduce a general chirp model and claim that it
includes all physically realistic GW chirps. We produce a finite grid of
template waveforms which samples the resulting set of possible chirps. If we
follow the classical approach (used for the detection of inspiralling binary
chirps, for instance), we would build a bank of quadrature matched filters
comparing the data to each of the templates of this grid. The detection would
then be achieved by thresholding the output, the maximum giving the individual
which best fits the data. In the present case, this exhaustive search is not
tractable because of the very large number of templates in the grid. We show
that the exhaustive search can be reformulated (using approximations) as a
pattern search in the time-frequency plane. This motivates an approximate but
feasible alternative solution which is clearly linked to the optimal one.
[abridged version of the abstract]Comment: 23 pages, 9 figures. Accepted for publication in Phys. Rev D Some
typos corrected and changes made according to referee's comment
Robustness maximization of parallel multichannel systems
Bit error rate (BER) minimization and SNR-gap maximization, two robustness
optimization problems, are solved, under average power and bit-rate
constraints, according to the waterfilling policy. Under peak-power constraint
the solutions differ and this paper gives bit-loading solutions of both
robustness optimization problems over independent parallel channels. The study
is based on analytical approach with generalized Lagrangian relaxation tool and
on greedy-type algorithm approach. Tight BER expressions are used for square
and rectangular quadrature amplitude modulations. Integer bit solution of
analytical continuous bit-rates is performed with a new generalized secant
method. The asymptotic convergence of both robustness optimizations is proved
for both analytical and algorithmic approaches. We also prove that, in
conventional margin maximization problem, the equivalence between SNR-gap
maximization and power minimization does not hold with peak-power limitation.
Based on a defined dissimilarity measure, bit-loading solutions are compared
over power line communication channel for multicarrier systems. Simulation
results confirm the asymptotic convergence of both allocation policies. In non
asymptotic regime the allocation policies can be interchanged depending on the
robustness measure and the operating point of the communication system. The low
computational effort of the suboptimal solution based on analytical approach
leads to a good trade-off between performance and complexity.Comment: 27 pages, 8 figures, submitted to IEEE Trans. Inform. Theor
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