Efficient strategies and techniques for structured data are key in modern computer science to design fast algorithms useful in a variety of every day applications (like web archiving, mail servers, network routers and video games). This course explores selected topics on advanced data structures including multidimensional and metric data structures, self-adjusting and probabilistic and randomized data structures. The course covers, for each topic, the fundamental concepts, major results and techniques of analysis.
Preliminares matemáticos. Diccionarios probabilísticos y randomizados. Priority queues y mfsets. Estructuras de dato multidimensionales y métricas. Tópicos avanzados selectos:
self-adjusting trees, digital search trees.
Se sugiere:
Conocimientos elementales de teoría de la probabilidad.
Conocimiento de estructuras de datos.
H. Samet: Foundations of multidimensional and metric data structures. Elsevier & Morgan Kaufmann, (2006).
T.H. Cormen et al.: Introduction to algorithms (4th ed). MIT Press, (2022).
D. Mehta et al.: Handbook of Data Structures and Applications. Chapman&Hall/CRC Press, (2005).
D. Gusfeld: Algorithms on strings, trees, and sequences. Cambridge University Press, (1997).
Desarrollado por el Departamento de Computación, FCEyN, UBA