I suspect that students learn more from our programming assignments than from our much sweated-over lectures, with their slide transitions, clip art, and joke attempts. A great assignment is deliberate about where the student hours go, concentrating the student's attention on material that is interesting and useful. The best assignments solve a problem that is topical and entertaining, providing motivation for the whole stack of work. Unfortunately, creating great programming assignments is both time consuming and error prone. The Nifty Assignments special session is all about promoting and sharing the ideas and ready-to-use materials of successful assignments.

Graph cuts have become an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields. In this paper, the graph cut problem is reformulated as an unconstrained l1 norm minimization that can be solved effectively using interior point methods. This reformulation exposes connections between the graph cuts and other related continuous optimization problems. Eventually the problem is reduced to solving a sequence of sparse linear systems involving the Laplacian of the underlying graph. The proposed procedure exploits the structure of these linear systems in a manner that is easily amenable to parallel implementations. Experimental results obtained by applying the procedure to graphs derived from image processing problems are provided.