Comparison of iterative matrix methods for information retrieval

This thesis describes the topic of information retrieval and introduces iterative matrix algorithms useful in this context - the Lanczos algorithm used in latent semantic index- ing and the Golub-Kahan-Lanczos bidiagonalization. The efficiency of these techniques is compared in a series of numerical experiments which measure retrieval performance and computation time on collection of real-world datasets. The study of the methods is conducted in both single and double IEEE precision arithmetic, and special attention is payed to the variations. The results of our experiments suggest that in many cases, lower precision can be used without significantly damaging retrieval performance. This finding opens the door to future investigation into the possible use of lower precision in informa- tion retrieval. Provided is also a series of codes in the MATLAB programming language as well as the preprocessed datasets, both of which were utilized in the experiments.