It is well understood and has been well documented that there is much to gain by using live projects in the computer science classroom [1-13]. Live projects include those that serve a real client with a real problem as well as those those that integrate live datasets. However, the use of live projects has always come with a variety of challenges including supporting the project once it is complete, providing secure and safe access to large data sets, adjusting live client expectations, and limiting the scope of a project to the context of a single term and within the learning outcomes. The purpose of this special session is to provide attendees an introduction to and examples of the way that live projects have been integrated throughout the computing curriculum at Radford University.

Structural approximation theory seeks to provide a framework for expressing optimization problems, and isolating structural or syntactic conditions that explain the (apparent) difference in the approximation properties of different NP-optimization problems. In this paper, we initiate a study of structural approximation using integer programming (an optimization problem in its own right) as a general framework for expressing optimization problems. We isolate three classes of constant-approximable maximization problems, based on restricting appropriately the syntactic form of the integer programs expressing them. The first of these classes subsumes MAX /spl Sigma//sub 1/, which is the syntactic version of the well-studied class MAX NP. Moreover, by allowing variables to take on not just 0/1 values but rather values in a polylogarithmic or polynomial range, we obtain syntactic maximization classes that are polylog-approximable and poly-approximable, respectively. The other two classes contain problems, such as MAX MATCHING, for which no previous structural explanation of approximability has been found.