Exploring Css 413 1 Pseudorandomness Lecture 6
Let's dive into the details surrounding Css 413 1 Pseudorandomness Lecture 6.
- Instructor: Prahladh Harsha Introduction, Administrivia, The Power of Randomness, Is Randomness Essential? Can Randomness ...
- Instructor: Prahladh Harsha Agenda: Randomized Complexity classes, Error Reduction, Basic Probability Inequalities, Sampling.
- Instructor: Prahladh Harsha Agenda: [The Saks-Zhou theorem] BPL is in DSPACE((log n)^{1.5})
- Instructor: Ramprasad Saptharishi Agenda: [Introduction to expansion] Vertex expansion, spectral expansion, connection between ...
- Instructor: Prahladh Harsha Agenda: Samplers,
In-Depth Information on Css 413 1 Pseudorandomness Lecture 6
Instructor: Prahladh Harsha Agenda: promise problems, samplers as hypergraphs, towards graph expansion. Instructor: Prahladh Harsha Agenda: vertex expansion, random graphs are vertex expanders, KPS error-reduction for RP. Instructor: Ramprasad Saptharishi Agenda: Introduction to the course, administrivia, general notion of Instructor: Prahladh Harsha Agenda: Randomness elimination/reduction via enumeration, method of conditional expectations and ...
Instructor: Prahladh Harsha Agenda: [Spectral expanders] Random walk matrix, second eigenvalue, expander mixing lemma, ...
That wraps up our extensive overview of Css 413 1 Pseudorandomness Lecture 6.
