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352
A Soft Constraint of Equality: Complexity and Approximability
, 2008
"... 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 of pairs ..."
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Cited by 1 (1 self)
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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
Soft Constraints of Difference and Equality
, 2011
"... In many combinatorial problems one may need to model the diversity or similarity of sets of assignments. For example, one may wish to maximise or minimise the number of distinct values in a solution. To formulate problems of this type we can use soft variants of the well known AllDifferent and AllEq ..."
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Cited by 3 (0 self)
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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
Reasoning about soft constraints and conditional preferences: Complexity results and approximation techniques
 In Proceedings of IJCAI2003
, 2003
"... Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CPnets and soft constraints, that handles bot ..."
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Cited by 40 (16 self)
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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
Low Complexity SoftInput SoftOutput Block Decision Feedback Equalization
, 2008
"... A low complexity softinput softoutput (SISO) block decision feedback equalizer (BDFE) is presented for turbo equalization. The proposed method employs a suboptimum sequencebased detection, where the softoutput of the equalizer is calculated by evaluating an approximation of the sequencebased ..."
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A low complexity softinput softoutput (SISO) block decision feedback equalizer (BDFE) is presented for turbo equalization. The proposed method employs a suboptimum sequencebased detection, where the softoutput of the equalizer is calculated by evaluating an approximation of the sequence
ReducedComplexity SISO Equalization for Rayleigh Fading Channels with Known Statistics
, 2004
"... For Rayleigh fading channels with known covariance matrix, reducedcomplexity receivers can be derived which approximate the maximum likelihood (ML) receiver by defining additional constraints. The reducedcomplexity receiver, which assumes that the current channel output depends on finite previous ..."
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Cited by 1 (0 self)
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For Rayleigh fading channels with known covariance matrix, reducedcomplexity receivers can be derived which approximate the maximum likelihood (ML) receiver by defining additional constraints. The reducedcomplexity receiver, which assumes that the current channel output depends on finite previous
Descriptive complexity of approximate counting
"... Motivated by Fagin’s characterization of NP, Saluja et al. have introduced a logic based framework for expressing counting problems. In this setting, a counting problem (seen as a mapping C from structures to nonnegative integers) is ’defined ’ by a firstorder sentence ϕ if for every instance A of ..."
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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
A Scheme for Approximating Probabilistic Inference
 In Proceedings of Uncertainty in Artificial Intelligence (UAI97
, 1997
"... 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 hyp ..."
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Cited by 57 (20 self)
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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
A CONTROLLABLE COMPLEXITY SOFTOUTPUT SUBOPTIMAL CONVOLUTIONAL DECODER
"... Suboptimal decoding of convolutional codes, motivated by the need to deal with large constraint length codes, has in the past been achieved by stack algorithms or sequential decoding, which typically do not produce soft outputs, which may be desirable in some modern iterative decoding frameworks. Mo ..."
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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 softoutput decoder. Initial
Descriptive complexity of approximate counting CSPs
, 2013
"... Motivated by Fagin’s characterization of NP, Saluja et al. have introduced a logic based framework for expressing counting problems. In this setting, a counting problem (seen as a mapping C from structures to nonnegative integers) is ’defined ’ by a firstorder sentence ϕ if for every instance A of ..."
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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
A LINEAR COMPLEXITY TURBO EQUALIZER BASED ON A MODIFIED SOFT INTERFERENCE CANCELLER
"... SoftInput SoftOutput (SISO) equalizers based on linear filters have proven to be good, low complexity, alternatives to trellisbased SISO equalizers. In particular, the Soft Interference Canceller (SIC) has recently received great interest, especially for receivers performing Turbo Equalization ..."
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expectation of those symbols, given both the apriori probabilities and the received sequence. This modification results in performance gains at the expense of increased computational complexity. However, by introducing an approximation to the aforementioned algorithm a linear complexity SISO equalizer can
Results 1  10
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352