PalVerFeb2007.pdf
... ) or, equivalently, the symbol-wise a posteriori probabilities (APP) obtained by an optimum soft decoder. As is well known, in some notable cases of interest, the APPs can be computed or approximated very efficiently in practice by message-passing algorithms. For example, for Markov sources (e.g., c ...
... ) or, equivalently, the symbol-wise a posteriori probabilities (APP) obtained by an optimum soft decoder. As is well known, in some notable cases of interest, the APPs can be computed or approximated very efficiently in practice by message-passing algorithms. For example, for Markov sources (e.g., c ...
The expansion of random regular graphs
... depends only upon d. (In fact, we will see that we can take cd ≥ 0.18 for all d ≥ 3, and cd → 1/2 as d → ∞.) More precisely, if G(n, d) denotes a (uniform) random d-regular graph on [n], meaning a (labelled) d-regular graph on [n] chosen uniformly at random from the set of all d-regular graphs on [n ...
... depends only upon d. (In fact, we will see that we can take cd ≥ 0.18 for all d ≥ 3, and cd → 1/2 as d → ∞.) More precisely, if G(n, d) denotes a (uniform) random d-regular graph on [n], meaning a (labelled) d-regular graph on [n] chosen uniformly at random from the set of all d-regular graphs on [n ...
APPROXIMATING THE MINIMUM SPANNING TREE WEIGHT IN SUBLINEAR TIME
... of the optimal solution, for example, the size of a maxcut, without computing the structure that achieves it, i.e., the actual cut. Sometimes, however, a solution can also be constructed in linear or near-linear time. In this paper, we consider the problem of finding the weight of the minimum spannin ...
... of the optimal solution, for example, the size of a maxcut, without computing the structure that achieves it, i.e., the actual cut. Sometimes, however, a solution can also be constructed in linear or near-linear time. In this paper, we consider the problem of finding the weight of the minimum spannin ...
A simple D -sampling based PTAS for k-means and other Clustering problems
... centers as seeds. Based on these k centers, partition the set of points into k clusters, where each point gets assigned to the closest center. Now, we update the set of centers as the means of each of these clusters. This process is repeated till we get convergence. Although, this heuristic often pe ...
... centers as seeds. Based on these k centers, partition the set of points into k clusters, where each point gets assigned to the closest center. Now, we update the set of centers as the means of each of these clusters. This process is repeated till we get convergence. Although, this heuristic often pe ...
Food Security As Resilience - Christopher B. Barrett
... Prevalence of food (in)security, or population with an acceptable probability of falling (below)above a given health/nutrition threshold over time For individuals or any aggregate (entire sample, female headed households, specific livelihood group…) Satisfies all four axioms of food security m ...
... Prevalence of food (in)security, or population with an acceptable probability of falling (below)above a given health/nutrition threshold over time For individuals or any aggregate (entire sample, female headed households, specific livelihood group…) Satisfies all four axioms of food security m ...
