CS 852: Computational Geometry

CSL 852: Computational Geometry

Semester I 2010-2011

Instructor : Sandeep Sen

Guest Lecturer: Pankaj Agarwal (Duke University)

Lectures:

Tue, Wed, Fri 5-6 pm (K slot) MS 107 (Video Recording Studio, next to the elevator)

Prerequisite: Algorithms and data structures.

Topics:

Computational geometry studies the design, analysis, and implementation of algorithms and data structures for geometric problems. These problems arise in a wide range of areas, including CAD/CAM, robotics, computer graphics, molecular biology, GIS, spatial databases, sensor networks, and machine learning. In addition to the tools developed in computer science, the study of geometric algorithms also requires ideas from various mathematical disciplines, e.g., combinatorics, topology, algebra, and differential geometry. This close interaction between various mathematical and practical areas has had a beneficial impact on both basic and applied research in computational geometry.

The goal of this course is to provide an overview of the techniques developed in computational geometry as well as some of its application areas. The topics covered in the course will include:

1. Geometric Fundamentals: Models of computation, lower bound techniques, geometric primitives, geometric transforms

2. Convex hulls: Planar convex hulls, higher dimensional convex hulls, randomized, output-sensitive, and dynamic algorithms, applications of convex hull

3. Intersection detection: segment intersection, line sweep, map overlay, halfspace intersection, polyhedra intersection

4. Geometric searching: segment, interval, and priority-search trees, point location, persistent data structure, fractional cascading, range searching, nearest-neighbor searching

5. Proximity problems: closest pair, Voronoi diagram, Delaunay triangulation and their subgraphs, spanners, well separated pair decomposition

6. Arrangements: Arrangements of lines and hyperplanes, sweep-line and incremental algorithms, lower envelopes, levels, and zones, applications of arrangements

7. Triangulations: monotone and simple polygon triangulations, point-set triangulations, optimization criteria, Steiner triangulation, Delaunay refinement

8. Geometric sampling: random sampling and ε-nets, ε-approximation and discrepancy, cuttings, coresets

9. Geometric optimization: linear programming, LP-type problems, parametric searching, approximation techniques

10. Implementation Issues : robust computing, perturbation techniques, floating-point filters, rounding techniques

Organization:

Grading will be based on a Midterm (25%), Major (40%) and Assignments (30%). Students will be expected to scribe lecture notes and prepare latex documents for a one/two lectures.

Attendence related Scribing and class participation will count for 5%. Note that students having less than 75% attendence will be reported for possible penal action according to the new attendence rules of the institute.