Algorithms and heuristics developed have been tested on random geometric graphs over a selection of topologies such as plane, sphere and torus. Some prior art solutions applicable to the planar topology have been extended to cover other topologies. In order to build geometric graphs on toroidal ...
Random geometric graphs1,2,3formalize the notion of “discretization” of a continuous geometric space or manifold. Nodes in these graphs are points, sprinkled randomly at constant sprinkling density, over the manifold, thus representing “atoms” of space, while links encode geometry—two nodes are...
GraphTheory[RandomGraphs] RandomGeometricGraph generate random geometric graph Calling Sequence Parameters Options Description Examples Compatibility Calling Sequence RandomGeometricGraph( n , t , dims , opts ) Parameters n - positive integer or list...
22 Weak solutions to the master equation of a potential mean field game 57:26 Controlling Human Microbiota 48:56 Describing interacting particle systems via partial differential equations and g 30:38 Differential Equations and Algebraic Geometry - 1 56:20 Epidemic Model-Based Benchmark for Optimal ...
26 On the Quality of the ABC-Solutions 39:35 Negative moments of the Riemann zeta-function 49:44 Least quadratic non-residue and related problems 45:43 Extreme Values of the Riemann Zeta Function and Dirichlet L-functions at the Cri 44:09 An extension of Venkatesh's converse theorem to the...
During recent decades, there has been much interest in finding optimal or near-optimal solutions to this problem. Many existing heuristic algorithms for MinVC are based on local search strategies. Recently, an algorithm called FastVC takes a first step towards solving the MinVC problem for large...
• For a quick guide to generating random numbers, arrays, permutations, graphs, polynomials, etc, see theHowDoI,WorkWithRandomGeneratorshelp page. • Each command in theRandomToolspackage can be accessed by using either thelong formor theshort formof the command name in the command calling ...
However, they can be solved if the input data is drawn randomly from a distribution over graphs containing acceptable solutions. In this paper we show that a simple spectral algorithm can solve all three problems above in the average case, as well as a more general problem of partitioning ...
We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network. The resulting optimization problem takes the form of constrained statistical learning, in which solutions can be found in a model-free manner by parameterizing the resource alloc...
In this section, we detail why SBM, LFR, and R-MAT do not provide solutions to the modeling task we aim to solve. For this purpose, we compare the outcome of these generators when fitted to idealized hyperbolic communities. One may argue that such communities are not realistic and therefore...