CRI: CRD-- An On-Demand Test Problem Server

算法研究人员需要实际的测试问题来验证他们的工作。然而，这些研究人员可能无法访问可能产生此类测试问题的应用程序。因此，他们不得不依赖于存储在web站点上的测试问题，这些站点的多样性可能太有限，而且小得不切实际，或者依赖于手工编码的测试问题，这是不现实的。本研究涉及测试问题服务器的构建，即安装了许多实际研究和生产代码的服务器，并具有将这些代码产生的测试问题传递给算法研究人员的设施。有了这样一个服务器，研究人员可以提前指定所需的测试问题的大小和性质，有利于算法研究的质量。线性系统、图论或特征值计算等算法通常是在数学和计算机科学部门开发的，而研究人员却无法访问这些算法将从中受益的应用程序。因此，算法研究人员必须依赖于流行的存档网站（如Matrix Market）上有限的测试问题清单，或者他们必须编写自己的测试问题，这是不太可能现实的。该项目构建了一个服务器，其中将安装一些研究和生产代码，并具有向用户提供生成问题的功能。这些代码可以按需运行，使用指定的问题输入来控制问题的大小和数值属性。因此，研究人员可以接触到范围广泛、数值性质精细分级的测试问题。这有利于所开发算法的质量和鲁棒性。

Algorithm researchers require realistic test problems to validate their work. However, these researchers may not have access to the applications that would produce such test problems. Consequently, they have to rely on test problems stored on web sites, which may be too limited in variety and unrealistically small, or on hand-coded test problems, which will not be realistic. This research concerns the construction of a test problem server, that is, a server that has a number of realistic research and production codes installed, with the facility to deliver to the algorithm researcher the test problems these codes produce. With such a server, the researcher can specify in advance the desired size and properties of the test problems, benefitting the quality of the algorithm research.Algorithms such as for linear systems, graph theory, or eigenvalue computations, are often developed in mathematics and computer science departments, without the researcher having access to the sort of applications that these algorithms will benefit from. Consequently, algorithm researchers have to rely on the limited inventory of test problems on popular archive sites such as Matrix Market, or they have to code their own test problems, which are unlikely to be realistic. This project constructs a server that will have a number of research and production codes installed, with the facility to serve up generated problems to the user. These codes can be run on demand, with specified problem inputs controlling the problem size and numerical properties. Thus, researchers have access to test problems that are both wide ranging in variety and finely graded in numerical properties. This is of benefit to the quality and robustness of the algorithms developed.