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，这些问题可能种类太少，而且不切实际地太小，或者依赖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，即安装了许多现实的研究和生产代码的服务器，具有向算法研究人员交付这些代码产生的测试问题的设施。例如用于线性系统、图论或特征值计算的算法通常是在数学和计算机科学系中开发的，而研究人员无法访问这些算法将从中受益的那类应用。因此，算法研究人员不得不依赖流行的归档网站(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，这不太可能实现。这个项目构建了一个服务器，它将安装一些研究和生产代码，并具有向用户提供所产生的问题的设施。这些代码可以按需运行，通过指定的问题输入控制问题的大小和数值特性。因此，研究人员可以接触到种类繁多、数值性质评分精细的测试题。这有利于所开发算法的质量和健壮性。

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.