Improved algorithms via random sampling

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Randomized methods have recently come into the spotlight when it comes to solving computationally "intractable" subset problems. The running time of a range of algorithms has been improved by replacing their first steps by a simple method of adding to the solution a small subset uniformly at random, and repeating the process many times. This project will explore various other ways how (not necessarily uniform) random sampling can improve the running time of algorithms; and explore the analysis of polynomial-time randomized algorithms. The project will focus on cycle cutsets and domination problems that have applications in operating systems, chip design and verification, facility location, and surveillance and monitoring.