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

算法研究人员需要现实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会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网站上的测试问题，这些问题可能在种类上过于有限，并且不切实际地小，或者依赖于手工编码的测试问题，这将是不现实的。本研究涉及的测试问题服务器的建设，也就是说，一个服务器，有一些现实的研究和生产安装的代码，与设施提供给算法研究人员的测试问题，这些代码产生。有了这样的服务器，研究人员可以预先指定测试问题的大小和属性，从而提高算法研究的质量。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，而研究人员无法访问这些算法将受益的应用程序。因此，算法研究人员不得不依赖于流行的存档网站（如Matrix Market）上有限的测试问题库存，或者他们必须编写自己的测试问题，这不太可能是现实的。 该项目构建了一个服务器，将有一些研究和生产代码安装，与设施，以提供产生的问题给用户。这些代码可以按需运行，指定的问题输入控制问题的大小和数值属性。因此，研究人员可以获得测试问题，这些问题既有广泛的多样性，又有精细的数值特性。这对所开发的算法的质量和鲁棒性是有益的。

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.