The results presented here are concerned with questions of decomposability of multiplicative semigroups of matrices with nonnegative entries. Chapter 1 covers some preliminary results which become useful in the remainder of the exposition. Chapters 2 and 3 constitute an exposition of some recent known results on special semigroups. Chapter 2 explores conditions for decomposability of semigroups in terms of conditions derived from linear functionals and in Chapter 3, we give a complete proof of an extension of the celebrated Perron-Frobenius Theorem. No originality is claimed for the results in Chapters 2 and 3. In Chapter 4, we present some new results on sufficient conditions for finiteness of semigroups of matrices.

The results presented here are concerned with questions of decomposability of multiplicative semigroups of matrices with nonnegative entries. Chapter 1 covers some preliminary results which become useful in the remainder of the exposition. Chapters 2 and 3 constitute an exposition of some recent known results on special semigroups. Chapter 2 explores conditions for decomposability of semigroups in terms of conditions derived from linear functionals and in Chapter 3, we give a complete proof of an extension of the celebrated Perron-Frobenius Theorem. No originality is claimed for the results in Chapters 2 and 3. In Chapter 4, we present some new results on sufficient conditions for finiteness of semigroups of matrices.

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Objects such as documents, people, words or utilities, that are related in some way, for instance by citations, friendship, appearance in definitions or physical connections, may be conveniently represented using graphs or networks. An increasing number of such relational databases, as for instance the World Wide Web, digital libraries, social networking web sites or phone calls logs, are available. Relevant information may be hidden in these networks. A user may for instance need to get authority web pages on a particular topic or a list of similar documents from a digital library, or to determine communities of friends from a social networking site or a phone calls log. Unfortunately, extracting this information may not be easy. This thesis is devoted to the study of problems related to information extraction in large graphs with the help of dominant vectors of nonnegative matrices. The graph structure is indeed very useful to retrieve information from a relational database. The correspondence between nonnegative matrices and graphs makes Perron--Frobenius methods a powerful tool for the analysis of networks. In a first part, we analyze the fixed points of a normalized affine iteration used by a database matching algorithm. Then, we consider questions related to PageRank, a ranking method of the web pages based on a random surfer model and used by the well known web search engine Google. In a second part, we study optimal linkage strategies for a web master who wants to maximize the average PageRank score of a web site. Finally, the third part is devoted to the study of a nonlinear variant of PageRank. The simple model that we propose takes into account the mutual influence between web ranking and web surfing.

The first part of the thesis studies invariant subspaces of strictly singular operators. By a celebrated result of Aronszajn and Smith, every compact operator has an invariant subspace. There are two classes of operators which are close to compact operators: strictly singular and finitely strictly singular operators. Pelczynski asked whether every strictly singular operator has an invariant subspace. This question was answered by Read in the negative. We answer the same question for finitely strictly singular operators, also in the negative. We also study Schreier singular operators. We show that this subclass of strictly singular operators is closed under multiplication by bounded operators. In addition, we find some sufficient conditions for a product of Schreier singular operators to be compact. The second part studies almost invariant subspaces. A subspace Y of a Banach space is almost invariant under an operator T if TY is a subspace of Y+F for some finite-dimensional subspace F ("error"). Almost invariant subspaces of weighted shift operators are investigated. We also study almost invariant subspaces of algebras of operators. We establish that if an algebra is norm closed then the dimensions of "errors" for the operators in the algebra are uniformly bounded. We obtain that under certain conditions, if an algebra of operators has an almost invariant subspace then it also has an invariant subspace. Also, we study the question of whether an algebra and its closure have the same almost invariant subspaces. The last two parts study collections of positive operators (including positive matrices) and their invariant subspaces. A version of Lomonosov theorem about dual algebras is obtained for collections of positive operators. Properties of indecomposable (i.e., having no common invariant order ideals) semigroups of nonnegative matrices are studied. It is shown that the "smallness" (in various senses) of some entries of matrices in an indecomposable semigroup of positive matrices implies the "smallness" of the entire semigroup. / Mathematics

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This is a literature study, in linear algebra, about positive and nonnegative matrices and their special properties. We say that a matrix or a vector is positive/nonnegative if all of its entries are positive/nonnegative. First, we study some generalities and become acquainted with two types of nonnegative matrices; irreducible and reducible. After exploring their characteristics we investigate and prove the two main theorems of this subject, namely Perron's and Perron-Frobenius' theorem. In short Perron's theorem from 1907 tells us that the spectral radius of a positive matrix is a simple eigenvalue of the matrix and that its eigenvector can be taken to be positive. In 1912, Georg Frobenius generalized Perron's results also to irreducible nonnegative matrices. The two theorems have a wide range of applications in both pure mathematics and practical matters. In real world scenarios, many measurements are nonnegative (length, time, amount, etc.) and so their mathematical formulations often relate to Perron-Frobenius theory. The theory's importance to linear dynamical systems, such as Markov chains, cannot be overstated; it determines when, and to what, an iterative process will converge. This result is in turn the underlying theory for the page-ranking algorithm developed by Google in 1998. We will see examples of all these applications in chapters four and five where we will be particularly interested in different types of Markov chains.  The theory in this thesis can be found in many books. Here, most of the material is gathered from Horn-Johnson [5], Meyer [9] and Shapiro [10]. However, all of the theorems and proofs are formulated in my own way and the examples and illustrations are concocted by myself, unless otherwise noted. / Det här är en litteraturstudie, inom linjär algebra, om positiva och icke-negativa matriser och deras speciella egenskaper. Vi säger att en matris eller en vektor är positiv/icke-negativ om alla dess element är positiva/icke-negativa. Inledningsvis går vi igenom några grundläggande begrepp och bekanta oss med två typer av icke-negativa matriser; irreducibla och reducibla. Efter att vi utforskat deras egenskaper så studerar vi och bevisar ämnets två huvudsatser; Perrons och Perron-Frobenius sats. Kortfattat så säger Perrons sats, från 1907, att spektralradien för en positiv matris är ett simpelt egenvärde till matrisen och att dess egenvektor kan tas positiv. År 1912 så generaliserade Georg Frobenius Perrons resultat till att gälla också för irreducibla icke-negativa matriser.  De två satserna har både många teoretiska och praktiska tillämpningar. Många verkliga scenarios har icke-negativa mått (längd, tid, mängd o.s.v) och därför relaterar dess matematiska formulering till Perron-Frobenius teori. Teorin är betydande även för linjära dynamiska system, såsom Markov-kedjor, eftersom den avgör när, och till vad, en iterativ process konvergerar. Det resultatet är i sin tur den underliggande teorin bakom algoritmen PageRank som utvecklades av Google år 1998. Vi kommer se exempel på alla dessa tillämpningar i kapitel fyra och fem, där vi speciellt intresserar oss för olika typer av Markov-kedjor. Teorin i den här artikeln kan hittas i många böcker. Det mesta av materialet som presenteras här har hämtats från Horn-Johnson [5], Meyer [9] och Shapiro [10]. Däremot är alla satser och bevis formulerade på mitt eget sätt och alla exempel, samt illustrationer, har jag skapat själv, om inget annat sägs.