An algorithm for computing the capacity of arbitrary discrete memoryless channels

By S. Arimoto. 1972

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In Information Theory

Citations in the corpus

Evaluating Lossy Compression Rates of Deep Generative Models (2020, methodology): “8 has been used in other algorithms such as the Blahut-Arimoto algorithm (Arimoto, 1972) or the variational information bottleneck algorithm (Alemi et al.”
Variational Intrinsic Control (2016, methodology): “This information measure has been introduced in the empowerment literature before (Salge et al., 2014; Klyubin et al., 2005) along with methods for measuring it (such as Blahut (1972); Arimoto (1972)).”
Mutual Information Neural Estimation (2018, methodology): “, 2000) uses the Blahut-Arimoto Algorithm (Arimoto, 1972), which”
Bandwidth Efficient and Rate-Matched Low-Density Parity-Check Coded Modulation (2015, methodology): “The optimal distribution PX∗ can be approximated numerically by using the Blahut-Arimoto algorithm [50], [51].”
A high-bias, low-variance introduction to Machine Learning for physicists (2018, unknown): “Otherwise, the optimization can be performed through a Blahut-Arimoto type iterative update scheme (Arimoto, 1972; Blahut, 1972).”
Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models (2007, methodology): “This iteration scheme is of the same type as the Blahut-Arimoto algorithm for computing the channel capacity [11, 12], and has similar convergence properties.”
Empowerment - an Introduction (2013, methodology): “In this case, the optimizing distribution needs to be determined using the standard Blahut-Arimoto (BA) algorithm (Blahut 1972; Arimoto 1972) which is an expectation maximizationtype algorithm for computing the channel capacity.”
PAC-Bayesian Analysis of Co-clustering and Beyond (2010, methodology): “To find a local minimum ofF(Q,β) we adopt an EM-like alternating projection procedure, very similar to the Blahut-Arimoto algorithm for minimization of the rate distortion function (Arimoto, 1972; Blahut, 1972; Cover and Thomas, 1991).”
Introduction to Data Compression (2005, background): “Finally, how do we find the rate distortion function? There are two ways: one is a computational approach developed by Arimoto [101] and Blahut [102].”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “1) Known Discrete Memoryless Distribution: When the relevant features V and the observation S is discrete and the joint distribution PS,V is known, the maximizing distributions PU|S in the IB problem in (30), can be efficiently found by an alternating optimization procedure similar to the expectatio”
Optimum power allocation for parallel Gaussian channels with arbitrary input distributions (2006, background): “, for every , this is (with the obvious exception of -PSK) in general suboptimal in terms of mutual information [35]–[37].”
Geometric Programming for Communication Systems (2005, background): “The primal and dual problems of C(S) can be simultaneously and efficiently solved through a primal-dual interior point algorithm [97, 21], which scales smoothly for different channels and alphabet sizes and provides an alternative to the classical Blahut–Arimoto algorithm [3, 19].”
Simulation-Based Computation of Information Rates for Channels With Memory (2006, background): “This bound is implicit in the classical papers by Blahut [10] and Arimoto [1].”
Probabilistic Constellation Shaping for Optical Fiber Communications (2019, methodology): “and the AIR under a transmission energy constraint, but the MB distribution is proven to maximize only the entropy, not the AIR, the latter being maximized using the Blahut-Arimoto algorithm [72], [73].”
New And Surprising Ways to Be Mean (2018, unknown): “The channel capacity underlying both empowerment types can be exactly determined using the Blahut-Arimoto algorithm [11], [12].”