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This year a theme will be "reasonable" models of input distributions; that is, under what input distributions will algorithms perform better than in the worst case? Are these distributions likely in practice, and how can we argue this? Oher special focii will depend on the interests of the class.
The goal is to provide enough background to allow students to use current results or software from computational geometry in their work or to begin pursuing research in the area.
Topics will include:
We will read chapters in the textbook as well as supplementary material that will be distributed in class. For all the readings, there will be short-answer questions which have to be answered in writing, in class on the day that the reading is due. This is to ensure that you do the reading and that we can have a meaningful discussion in class.
There will also be some homework problems reviewing the important concepts. These will give you practice in algorithm design and analysis. These homework problems, like the reading questions, will be due in class.
Each student will have to give a half-hour presentation some time during the quarter; we will make a schedule in the second week. The presentation will usually cover a paper or other reading supplementing the material that the whole class reads.
Each student will work on a project, chosen in consultation with the instructor. I will suggest some projects that several people can share, and students who have ideas of their own are encouraged to bring them up. The project must involve reading a reasonably deep paper which uses computational geometry; aside from that I am pretty flexible. Something related to your existing research or your ultimate research goals is good. Ideally the project should be the first steps towards a publishable result.