Algorithms are required to be “correct” and “fast”. In a wide variety of applications, these twin objectives are in conflict with each other. Fortunately,neither of these ideals are sacrosanct. Therefore we can often try to optimize one of these goals by incurring a small penalty on the other. This takes us to the field of Randomized Algorithms. Often, the randomized variants, in addition to being faster than their deterministic counterpart, are simpler to understand and implement. In this course, we will study this tradeoff between correctness and speed. We will be learning a number of methods to design and analyze randomized algorithms.
INTENDED AUDIENCE : Senior UG students, PG students and Ph.D candidates interested in computer science, combinatorics, etc.PRE-REQUISITES : Basic Understanding of Algorithms and ProbabilitylINDUSTRY SUPPORT : Google, Microsoft
INTENDED AUDIENCE : Senior UG students, PG students and Ph.D candidates interested in computer science, combinatorics, etc.PRE-REQUISITES : Basic Understanding of Algorithms and ProbabilitylINDUSTRY SUPPORT : Google, Microsoft
