We introduce the SoftAllEqual global constraint, which maximizes the number of equalities holding between pairs of assignments to a set of variables. We study the computational complexity of propagating this constraint, showing that it is intractable in general, since maximizing the number

constraints. We present a taxonomy of six soft global constraints, generated by combining the two latter ones and the two standard cost functions, which are either maximised or minimised. We characterise the complexity of achieving arc and bounds consistency on these constraints, resolving those cases

both hard and soft constraints as well as conditional preferences efficiently and uniformly. We study the complexity of testing the consistency of preference statements, and show how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving

A low complexity soft-input soft-output (SISO) block decision feedback equalizer (BDFE) is presented for turbo equalization. The proposed method employs a sub-optimum sequence-based detection, where the soft-output of the equalizer is calculated by evaluating an approximation of the sequence

For Rayleigh fading channels with known covariance matrix, reduced-complexity receivers can be derived which approximate the maximum likelihood (ML) receiver by defining additional constraints. The reduced-complexity receiver, which assumes that the current channel output depends on finite previous

of the problem, the number of possible satisfying assignments of the variables of ϕ in A is equal to C(A). The logic RHΠ1 has been introduced by Dyer et al. as in their study of the counting complexity class #BIS. The interest of the class #BIS relies in the fact that, it might be quite the case

This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approximation for several tasks: finding the most probable explanation, belief updating and finding the maximum a posteriori

that the central limit theorem does not apply. Instead, we invoke Gallager’s lemma to compute the distribution of the interfering terms. Under various assumptions of independence (resulting in different complexities) the channel posterior probability can be approximated, resulting in a soft-output decoder. Initial

of the problem, the number of possible satisfying assignments of the variables of ϕ in A is equal to C(A). The logic RHΠ1 has been introduced by Dyer et al. in their study of the counting complexity class #BIS. The interest in the class #BIS stems from the fact that, it is quite plausible that the problems

expectation of those symbols, given both the a-priori probabilities and the received sequence. This modification results in performance gains at the expense of increased computational complexity. How-ever, by introducing an approximation to the aforementioned algorithm a linear complexity SISO equalizer can