PyAMG is a library of Algebraic Multigrid (AMG) solvers with a convenient Python interface. What is AMG?¶ AMG is a multilevel technique for solving large-scale linear systems with optimal or near-optimal efficiency. Unlike geometric multigrid, AMG requires little or no geometric information about the underlying problem and develops a sequence of coarser grids directly from the input matrix. This feature is especially important for problems discretized on unstructured meshes and irregular grids.
This document provides an in-depth look at the process used in trying to solve real issues with the User Experience of a social bookmarking application. While it might be easy to simply take the first solution that works and assume that it’s the best solution, the first solution is very rarely the best solution. We found several solutions to several problems, and many of them worked and appeared to be decent solutions. It was only upon further investigation and doing more detailed research that we found hidden flaws in some solutions, issues with user satisfaction in other solutions, and even found some solutions that broke entirely under certain conditions.
This paper will describe the problems we faced in detail and then provide an explanation of the solutions evaluated for each problem, including the benefits and drawbacks of each solution. We will also identify the final solution chosen and why it was chosen.
Netlib is a collection of mission-critical software components for linear algebra systems (i.e. working with vectors or matrices). Netlib libraries are written in C, Fortran or optimised assembly code. A Java translation has been provided by the F2J project but it does not take advantage of optimised system libraries.
K. Verichev, P. Mikhaylyukova, A. Salimova, C. Salazar, und M. Carpio. (2019)cited By 0; Conference of 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference Date: 28 July 2019 Through 2 August 2019; Conference Code:154792.
D. Hepting, H. Bin Amer, und Y. Yao. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, Volume 854 von Communications in Computer and Information Science, Seite 528--537. Cham, Springer International Publishing, (Juni 2018)