Learn what a randomized algorithm is, how it differs from a deterministic algorithm, what are some common types of randomized algorithms, and how you can use them in your code.

Learn how to design a randomized algorithm for hard problems, and explore some techniques and examples of using randomness in algorithm design.

This paper presents a new randomized algorithm for quickly finding approximate nearest neighbor matches between image patches. Our algorithm offers substantial performance improvements over the ...

As both the number and size of satellite constellations continue to increase, there likewise exists a growing need for incorporating methods for autonomous sensor selection into these networks.

We describe two recently proposed randomized algorithms for the construction of low-rank approximations to matrices, and demonstrate their application (inter alia) to the evaluation of the singular ...

We introduce a randomized algorithm for overdetermined linear least-squares regression. Given an arbitrary full-rank m × n matrix A with m ≥ n, any m × 1 vector b, and any positive real number ε, t ...

AA Second Project - The objective of this project was to design and test a randomized algorithm to solve the combinatorial problem from the first assignment, which was to find a minimum weighted ...

Randomized algorithms make random decisions rather than deterministic decisions. The main advantage is that no input can reliably produce worst-case results because the algorithm runs differently each ...

We propose a randomized algorithm of spectral clustering and apply it to appearance-based image/video segmentation. Spectral clustering is a kernel-based method of grouping data on separate nonlinear ...

There's lots of things you shouldn't leave up to people's best guesses, and determining which criminals are likely to reoffend is one of them.