A PageRank Algorithm based on Asynchronous Gauss-Seidel Iterations

Traditional PageRank -Compute relative importance of each webpage in the network. Ranking of Researchers - PageRank has also been proposed to be an alternative to citation count. General Ranking - Since PageRank is equivalent to the relative time spent in a node for a random walk on the graph many more applications are possible

OPTool - Documentation v1.2

The OPTool package is an implementation of various state-of-the-art iterative optimization algorithms for differentiable cost functions along with algorithms to solve linear equations. Users can use the toolbox to solve optimization problems, although the code was written to researchers that want to compare their proposals with state-of-the-art implementation. New algorithms can be easily added and the software will be updated to have the most comprehensive list of solvers possible. It also comes with implemented functions to return optimal parameters for these algorithms based on a control-theoretical formulation of the algorithms

Rayleigh quotient with bolzano booster for faster convergence of dominant eigenvalues

Computation ranking algorithms are widely used in several informatics fields. One of them is the PageRank algorithm, recognized as the most popular search engine globally. Many researchers have improvised the ranking algorithm in order to get better results. Recent research using Rayleigh Quotient to speed up PageRank can guarantee the convergence of the dominant eigenvalues as a key value for stopping computation. Bolzano's method has a convergence character on a linear function by dividing an interval into two intervals for better convergence. This research aims to implant the Bolzano algorithm into Rayleigh for faster computation. This research produces an algorithm that has been tested and validated by mathematicians, which shows an optimization speed of a maximum 7.08% compared to the sole Rayleigh approach. Analysis of computation results using statistics software shows that the degree of the curve of the new algorithm, which is Rayleigh with Bolzano booster (RB), is positive and more significant than the original method. In other words, the linear function will always be faster in the subsequent computation than the previous method

A PageRank Algorithm based on Asynchronous Gauss-Seidel Iterations

We address the PageRank problem of associating a relative importance value to all web pages in the Internet so that a search engine can use them to sort which pages to show to the user. This precludes finding the eigenvector associated with a particular eigenvalue of the link matrix constructed from the topology graph of the web. In this paper, we investigate the potential benefits of addressing the problem as a solution of a set of linear equations. Initial results suggest that using an asynchronous version of the Gauss-Seidel method can yield a faster convergence than using the traditional power method while maintaining the communications according to the sparse link matrix of the web and avoiding the strict sequential update of the Gauss-Seidel method. Such an alternative poses an interesting path for future research given the benefits of using other more advanced methods to solve systems of linear equations. Additionally, it is investigated the benefits of having a projection after all page ranks have been updated as to maintain all its entries summing to one and positive. In simulations, it is provided evidence to support future research on approximation rules that can be used to avoid the need for the projection to the $n$-simplex (the projection represents in some cases a threefold increase in the convergence rate over the power method) and on the loss in performance by using an asynchronous algorithm