Daniel Fullmer

2papers

2 Papers

OCMay 25, 2016
A Distributed Algorithm for Computing a Common Fixed Point of a Family of Paracontractions

Daniel Fullmer, Lili Wang, A. Stephen Morse

A distributed algorithm is described for finding a common fixed point of a family of $m>1$ nonlinear maps $M_i : \mathbb{R}^n \rightarrow \mathbb{R}^n$ assuming that each map is a paracontraction and that such a common fixed point exists. The common fixed point is simultaneously computed by $m$ agents assuming each agent $i$ knows only $M_i$, the current estimates of the fixed point generated by its neighbors, and nothing more. Each agent recursively updates its estimate of the fixed point by utilizing the current estimates generated by each of its neighbors. Neighbor relations are characterized by a time-dependent directed graph $\mathbb{N}(t)$ whose vertices correspond to agents and whose arcs depict neighbor relations. It is shown that for any family of paracontractions $M_i, i \in \{1,2,\ldots,m\}$ which has at least one common fixed point, and any sequence of strongly connected neighbor graphs $\mathbb{N}(t)$, $t=1,2,\ldots$, the algorithm causes all agent estimates to converge to a common fixed point.

CRDec 27, 2018
Analysis of Difficulty Control in Bitcoin and Proof-of-Work Blockchains

Daniel Fullmer, A. S. Morse

This paper presents a stochastic model for block arrival times based on the difficulty retargeting rule used in Bitcoin, as well as other proof-of-work blockchains. Unlike some previous work, this paper explicitly models the difficulty target as a random variable which is a function of the previous block arrival times and affecting the block times in the next retargeting period. An explicit marginal distribution is derived for the time between successive blocks (the blocktime), while allowing for randomly changing difficulty. This paper also aims to serve as an introduction to Bitcoin and proof-of-work blockchains for the controls community, focusing on the difficulty retargeting procedure used in Bitcoin.