Allocation problems have been central to the development of the theory of algorithms and also find applications in several realms of computer science and economics. In this thesis we initiate a systematic study of these problems in situations with limited information. Towards this end we explore several modes by which data may be obfuscated from the algorithm. We begin by investigating temporal constraints where data is revealed to the algorithm over time. Concretely, we consider the online bipartite matching problem in the unknown distribution model and present the first algorithm that breaches the 1-1/e barrier for this problem. Next we study issues arising from data acquisition costs that are prevalent in ad-systems and kidney exchanges. Motivated by these constraints we introduce the query-commit model and present constant factor algorithms for the maximum matching and the adwords problem in this model. Finally we assess the approximability of several classical allocation problems with multiple agents having complex non-linear cost functions. This presents an additional obstacle since the support for the cost functions may be extremely large entailing oracle access. We show tight information theoretic lower bounds for the general class of submodular functions and also extend these results to get lower bounds for a subclass of succinctly representable non-linear cost functions.

In the past decades, linear programming (LP) has been successfully used to develop approximation algorithms for various optimization problems. In particular, the so-called assignment LP has lead to substantial progress for various allocation problems, including scheduling unrelated parallel machines. However, we have reached its limits for many problems, since the best-known approximation algorithms match the integrality gap of the assignment LP for these problems. The natural question is then whether a different LP formulation can lead to better algorithms. We answer this question positively for variants of two allocation problems: max-min fair allocation and maximum budgeted allocation. This is achieved by using a more powerful LP formulation called the configuration LP that has an exponential number of variables, but can be approximated in polynomial time. The restricted max-min fair allocation problem, also known as the restricted Santa Claus problem, is one of few problems that have a better polynomial estimation algorithm than approximation algorithm. An estimation algorithm estimates the value of the optimal solution, but is not necessarily able to find the optimal solution. The configuration LP can be used to estimate the optimal value within a factor of 1/(4+ɛ) for any ɛ&gt;0, but it is only known how to efficiently find a solution achieving this value in exponential time. We give an approximation algorithm with the same approximation ratio but improve the running time to quasi-polynomial: n^O(log n). Our techniques also have the interesting property that although we use the rather complex configuration LP in the analysis, we never actually solve it and therefore the resulting algorithm is purely combinatorial. For the maximum budgeted allocation (MBA) the integrality gap of the assignment LP is exactly 3/4. We prove that the integrality gap of the configuration LP is strictly better than 3/4 and provide corresponding polynomial time rounding algorithms for two variants of the problem: the restricted MBA and the graph MBA. Finally, we improve the best-known upper bound on the integrality gap for the general case from 0.833 to 0.828 and also show hardness of approximation results for both variants studied. / Under de senaste decennierna har linjärprogrammering (LP) framgångsrikt använts för att utveckla approximeringsalgoritmer. I synnerhet har det så kallade tilldelnings-LP lett till betydande framsteg för olika allokeringsproblem, som scheduling unrelated parallel machines. Vi verkar dock ha nått dess gräns, eftersom de bästa approximeringsalgoritmerna har samma kvalitet som heltalsgapet för dessa problem. Den naturliga frågan är då om någon annan LP-formulering kan leda till bättre algoritmer. Vi besvarar denna fråga positivt för varianter av två fördelningsproblem: max-min fair allocation och maximal budgeted allocation. Vi använder en mer kraftfull LP-formulering som kallas konfigurations-LP och har ett exponentiellt antal variabler men kan approximeras i polynomisk tid. Problemet restricted max-min fair allocation, som är även känt som restricted Santa Claus problem, är ett av få problem som har en bättre polynomisk värderingsalgoritm än approximeringsalgoritm. En värderingsalgoritm approximerar det optimala värdet, men hittar inte nödvändigtvis den optimala lösningen. Konfigurations-LP kan användas för att approximera det optimala värdet inom en faktor 1 / (4 + ɛ) för något ɛ &gt; 0, men man vet bara hur man hittar en lösning med sådan kvalitet i exponentiellt tid. Vi ger en approximeringsalgoritm med samma approximeringskvalitet men förbättrar tidskomplexitet till kvasipolynomisk: n^O(log n). Våra tekniker har också den intressanta egenskapen att även om vi använder det ganska komplext konfigurations-LP:t i analysen, löser vi aldrig det och vår algoritm är rent kombinatorisk. För maximal budgeted allocation (MBA) är heltalsgapet av tilldelnings-LP:et är precis 3/4. Vi bevisar att heltalsgapet av konfiguration-LP är strikt bättre än 3/4 och vi ger en motsvarande polynomisk avrundningsalgoritm för två varianter av problemet: restricted MBA och graph MBA. Slutligen förbättrar vi den bäst kända övre gränsen på heltalsgapet för det allmänna fallet från 0.833 till 0.828 samt ger approximeringssvårighetsresultat för båda två studerade varianter. / <p>QC 20150305</p> / ERC APPROXNP 226203

In this thesis we formulate joint cell, channel and power allocation problems within wireless communication networks. The objectives are to maximize the user with mini- mum data throughput (Shannon capacity) or to maximize the total system throughput, referred to as the max-min and max-sum problem respectively. The complexity is stud- ied together with proposed optimization- and heuristic-based approaches. In the first paper an overall joint cell, channel and power allocation max-min prob- lem is formulated. We show that the decision problem is NP-hard and that the op- timization problem is not approximable unless P is equal to NP, for instances with a sufficiently large number of channels. Further, it follows that for a feasible binary cell and channel allocation, the remaining continuous power allocation optimization problem is still not approximable unless P is equal to NP. In addition, it is shown that first-order optimality conditions give global optimum of the single channel power al- location optimization problem, although the problem is in general not convex. In the following two papers heuristics for solving the overall problem are proposed. In the second paper we consider the single channel problem with convex combinations of the max-min and the max-sum objective functions. This variable utility provides the ability of tuning the amount of fairness and total throughput. The third paper investi- gates the multiple channel setting. On a system with three cells, eight mobile users and three channels, we perform an exhaustive search over feasible cell and channel alloca- tions. The exhaustive search is then compared to the less computationally expensive heuristic approaches, presenting potential earnings to strive for. A conclusion is that several of the proposed heuristics perform very well. The final paper incorporates fixed relay stations into the overall joint cell, channel and power allocation max-min problem. The complexity is inherited from the formula- tion without relay stations. Further, we propose a heuristic channel allocation approach that shows good performance, compared to an optimization based approach, in numer- ical simulations on the relay setting. / Financial support by the Swedish Foundation for Strategic Research (SSF) QC 20110915

abstract: We live in a networked world with a multitude of networks, such as communication networks, electric power grid, transportation networks and water distribution networks, all around us. In addition to such physical (infrastructure) networks, recent years have seen tremendous proliferation of social networks, such as Facebook, Twitter, LinkedIn, Instagram, Google+ and others. These powerful social networks are not only used for harnessing revenue from the infrastructure networks, but are also increasingly being used as “non-conventional sensors” for monitoring the infrastructure networks. Accordingly, nowadays, analyses of social and infrastructure networks go hand-in-hand. This dissertation studies resource allocation problems encountered in this set of diverse, heterogeneous, and interdependent networks. Three problems studied in this dissertation are encountered in the physical network domain while the three other problems studied are encountered in the social network domain. The first problem from the infrastructure network domain relates to distributed files storage scheme with a goal of enhancing robustness of data storage by making it tolerant against large scale geographically-correlated failures. The second problem relates to placement of relay nodes in a deployment area with multiple sensor nodes with a goal of augmenting connectivity of the resulting network, while staying within the budget specifying the maximum number of relay nodes that can be deployed. The third problem studied in this dissertation relates to complex interdependencies that exist between infrastructure networks, such as power grid and communication network. The progressive recovery problem in an interdependent network is studied whose goal is to maximize system utility over the time when recovery process of failed entities takes place in a sequential manner. The three problems studied from the social network domain relate to influence propagation in adversarial environment and political sentiment assessment in various states in a country with a goal of creation of a “political heat map” of the country. In the first problem of the influence propagation domain, the goal of the second player is to restrict the influence of the first player, while in the second problem the goal of the second player is to have a larger market share with least amount of initial investment. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

La question de l'accès au soin est cruciale dans notre société moderne. Un effet évident de la demande accrue de services de santé est l'augmentation du taux d'occupation dans les hôpitaux. La principale différence entre la dispensation de soins à l'hôpital et au domicile est la suivante: le patient doit se déplacer et toutes les ressources nécessaires à son traitement se trouvent dans le même endroit, tandis que dans les soins délivrés au domicile, les ressources doivent être déplacées au chevet du patient. Il existe plusieurs défis afin de pouvoir réaliser ce changement. Dans cette thèse nous traitons trois problèmes importants dans la conception des structures de soins à domicile. D’abord, la localisation des structures en minimisant les coûts logistiques, où nous développons trois modèles incluant différentes caractéristiques comme du système de santé comme les coûts liés aux déplacements des ressources, la variation de la demande dans le temps et l’existence et évolution des ressource libérales. Ces modèles nous permettent de proposer des localisations robustes dans le temps tout en assurant une couverture maximale et en minimisant les coûts. La deuxième problématique consiste au choix des activités et couverture épidémiologique et spatiale en tenant compte différentes activités et types de ressources, les autorisations pour réaliser les pathologies et la couverture. Deux modèles développés nous ont permis montrer les effets sur l’affectation de la demande et le dimensionnement de ressources induits par changements dans les coûts des libéraux, salaires et d’autorisation de servir la demande. Le troisième problème et celui du dimensionnement de ressources avec incertitudes de demande (volume, épidémiologique et géographique) et le modèle proposé tient compte du problème sous-jacent de déplacement des ressources à l’aide d’une estimation de la tournée réalisée. / The issue of access to heamthcare is crucial in our modern society. One obvious effect of the augmentation of healthcare services demand is the increasing occupancy rates in hospitals. The main difference between the provision of care at the hospital and at home is as follows: the patient is at hospital and all the resources necessary for its treatment are in the same place, while in the care delivered at home, resources must be moved to the bedside. There are several challenges in order to achieve this change. In this thesis we address three important issues in the design of structures of home care. First, the location of structures minimizing logistics costs, where we develop three models with different features such as traveling costs of resources, changes in demand over time and evolution of freelance resources. These models allow us to provide robust location over time while ensuring maximum coverage and minimizing costs. The second issue is the choice of activities, epidemiological and spatial coverage, taking into account different types of activities and resources, permissions to serve some pathologies and coverage. Two models developed allow us to show the effects on the demand allocation and resources planning induced by changes in the costs of freelance professionals and authorization to serve some pathologies. The third problem is the dimensioning of resources with demand uncertainty (volume, epidemiological and geographical) and the proposed model takes into account the underlying problem of moving resources using an estimate of the routes performed.