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Mobile Agent Algorithms Versus Message Passing Algorithms
2006In this paper, we are interested in the computational power of a mobile agent system and, more particularly, in the comparison with a message passing system. First we give formal definitions. Then we explain how a mobile agent algorithm can be simulated by a message passing algorithm.We also prove that any message passing algorithm can be implemented ...
Chalopin, Jérémie +3 more
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2009
Constraint Satisfaction Problems (CSPs) are defined over a set of variables whose state must satisfy a number of constraints. We study a class of algorithms called Message Passing Algorithms, which aim at finding the probability distribution of the variables over the space of satisfying assignments.
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Constraint Satisfaction Problems (CSPs) are defined over a set of variables whose state must satisfy a number of constraints. We study a class of algorithms called Message Passing Algorithms, which aim at finding the probability distribution of the variables over the space of satisfying assignments.
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A review of message passing algorithms in estimation of distribution algorithms
Natural Computing, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Roberto Santana 0001 +2 more
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2001
This chapter introduces a framework for understanding probabilistic decoding, and applies it to the decoding of low-density parity check (LDPC) codes. Section 1. sets up the preliminaries for soft iterative decoding. Section 2. explains the message-passing algorithm using normal graphs. Section 3. presents some basic nodes and modules.
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This chapter introduces a framework for understanding probabilistic decoding, and applies it to the decoding of low-density parity check (LDPC) codes. Section 1. sets up the preliminaries for soft iterative decoding. Section 2. explains the message-passing algorithm using normal graphs. Section 3. presents some basic nodes and modules.
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Upscaling message passing algorithms
2023The development of Approximate Message Passing (AMP) has become a precedent demonstrating the potential of Message Passing (MP) algorithms in solving large-scale linear inverse problems of the form y = A x + w. Not only AMP is provably convergent and Bayes-optimal, but it is also a first-order iterative method that can leverage Plug-and-Play (PnP ...
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Performance analysis of algorithms on a message passing multiprocess
[1991] Proceedings. The Fifth International Parallel Processing Symposium, 2002The paper discusses a model for analysing the performance of algorithms executed on a message passing multiprocessor which achieve reduction in parallel execution time by domain decomposition. The model can estimate the performance of algorithms with different execution behaviours and incorporates the effect of overlapping of computation and ...
Anupam Basu +4 more
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Message Passing Algorithm for GFDM-IM Detection
2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018The generalized frequency division multiplexing with index modulation (GFDM-IM) is a recently developed multi-carrier technique, which has the signal feature that only part of subcarriers are activated. In this paper, the message passing (MP)-based signal detection of GFDM-IM is studied, and two MP detectors are presented.
Yuan Niu, Jianping Zheng
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Message Passing Algorithms for MLS-3LIN Problem
Algorithmica, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Dynamics of Damped Approximate Message Passing Algorithms
2019 IEEE Information Theory Workshop (ITW), 2019For linear system models, the approximate massage passing (AMP) is one of the effective iterative sparse recovery algorithms. However, depending on a measurement matrix ensemble, AMP may face convergence issues. Some algorithms are proposed so far to avoid the convergence issues, e.g., the orthogonal AMP (OAMP) and the mean removal. One of the simplest
Kazushi Mimura, Jun'ichi Takeuchi
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A message-passing algorithm with damping
Journal of Statistical Mechanics: Theory and Experiment, 2005We propose a modified belief propagation algorithm, with over-relaxed dynamics. Such an algorithm turns out to be generally more stable and faster than ordinary belief propagation. We characterize the performance of the algorithm, employed as a tool for combinatorial optimization, on the random satisfiability problem.
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