# jemdoc: menu{MENU}{theses.html}, showsource 
= Damiano Varagnolo

#include{header}

[MSTheses/MS%20Thesis%20Proposal:%20Automatic%20reduction%20of%20Computational%20Fluid%20Dynamic%20models%20into%20Control-oriented%20models.pdf /Automatic reduction of Computational Fluid Dynamic models into Control-oriented models/]

[MSTheses/MS%20Thesis%20Proposal:%20Identification%20of%20quantitative%20models%20of%20mycelia%20and%20mushrooms%20growth%20dynamics.pdf /Identification of quantitative models of mycelia and mushrooms growth dynamics/]

[MSTheses/MS%20Thesis%20Proposal:%20Triangulation%20Lidars%20as%20People%20Counters.pdf /Triangulation Lidars as People Counters/]


# == Distributed optimization
# 
# ~~~
# {Fast Distributed Newton-Raphson methods}
# 
# *Background:* distributed optimization algorithms are basic building blocks for the management of networked systems. Solve optimization problems in fact means to solve estimation and decision problems. A general need is to build distributed optimization algorithms that are faster and faster, so that decisions can be taken in the smallest amount of time.
# 
# *Problem statement:* this thesis considers a particular distributed optimization technique, called [Publications/Varagnolo%20et%20al.%20-%202013%20-%20Newton-Raphson%20Consensus%20for%20Distributed%20Convex%20Optimization.pdf Newton-Raphson consensus]. The technique is characterized by a parameter, say $\varepsilon$, with the following characteristic: choosing a large $\varepsilon$ implies having a higher risk of divergence (i.e., of having the algorithm returning a completely meaningless result) but, at the same time, if the algorithm converges it converges faster. The tradeoff is thus /risk of divergence/ vs. /convergence speed/.
# 
# The thesis focuses on analyzing strategies of choosing dynamically $\varepsilon$ so that the risk of divergence is zero but, at the same time, the algorithm is as fast as possible.
# 
# *Contacts:*
# - Damiano Varagnolo, damiano.varagnolo@ltu.se
# 
# *References:*
# - [Publications/Varagnolo%20et%20al.%20-%202013%20-%20Newton-Raphson%20Consensus%20for%20Distributed%20Convex%20Optimization.pdf D. Varagnolo, F. Zanella, A. Cenedese, G. Pillonetto, L. Schenato, /Newton-Raphson Consensus for Distributed Convex Optimization/, IEEE Transactions on Automatic Control (submitted)]
# - [http://www.eecs.northwestern.edu/~morales/PSfiles/2156954.pdf R. S. Dembo, S. C. Eisenstat, T. Steihaug, /Inexact newton methods/, SIAM Journal on Numerical analysis, 1982]
# ~~~

# triangulation lidars:
# - people counters
# - forestry
# 
# occupancy:
# - build people counters
# - stuff with sensors from Swegon
# 
# estimation:
# - maps building with fast consensus
# - 
#