2 research outputs found
Consensus with Max Registers
We consider the problem of implementing randomized wait-free consensus from max registers under the assumption of an oblivious adversary. We show that max registers solve m-valued consensus for arbitrary m in expected O(log^* n) steps per process, beating the Omega(log m/log log m) lower bound for ordinary registers when m is large and the best previously known O(log log n) upper bound when m is small. A simple max-register implementation based on double-collect snapshots translates this result into an O(n log n) expected step implementation of m-valued consensus from n single-writer registers, improving on the best previously-known bound of O(n log^2 n) for single-writer registers
Notes on Randomized Algorithms
Lecture notes for the Yale Computer Science course CPSC 469/569 Randomized
Algorithms. Suitable for use as a supplementary text for an introductory
graduate or advanced undergraduate course on randomized algorithms. Discusses
tools from probability theory, including random variables and expectations,
union bound arguments, concentration bounds, applications of martingales and
Markov chains, and the Lov\'asz Local Lemma. Algorithmic topics include
analysis of classic randomized algorithms such as Quicksort and Hoare's FIND,
randomized tree data structures, hashing, Markov chain Monte Carlo sampling,
randomized approximate counting, derandomization, quantum computing, and some
examples of randomized distributed algorithms