Online coloring problem with a randomized adversary and infinite advice

Online problems are those in which the instance is not given as a whole but by parts named requests. They arrise naturaly in computer science. Several examples are given such as ski rental problem, the server problem and the coloring problem. The performance of the online algorithms is analized in terms of the ratio between the cost of the algorithm and the cost of the optimal offline. This ratio is called the competitive ratio. Several models of online algorithms are described. They are deterministic algorithms, randomized algorithms and algorithms with advice. We present several upper and lower bounds for the competitive ratio in a particular case of the k-server problem. We review the known bounds for the coloring problem in the diferent models. We present a new model, the randomized adversary. For this model we present an upper bound and a restricted lower bound. Finally we conjecture an unrestricted lower bound and we present several approaches to the result

Online coloring problem with a randomized adversary and infinite advice

Online problems are those in which the instance is not given as a whole but by parts named requests. They arrise naturaly in computer science. Several examples are given such as ski rental problem, the server problem and the coloring problem. The performance of the online algorithms is analized in terms of the ratio between the cost of the algorithm and the cost of the optimal offline. This ratio is called the competitive ratio. Several models of online algorithms are described. They are deterministic algorithms, randomized algorithms and algorithms with advice. We present several upper and lower bounds for the competitive ratio in a particular case of the k-server problem. We review the known bounds for the coloring problem in the diferent models. We present a new model, the randomized adversary. For this model we present an upper bound and a restricted lower bound. Finally we conjecture an unrestricted lower bound and we present several approaches to the result

Online graph coloring against a randomized adversary

Electronic version of an article published as Online graph coloring against a randomized adversary. "International journal of foundations of computer science", 1 Juny 2018, vol. 29, núm. 4, p. 551-569. DOI:10.1142/S0129054118410058 © 2018 copyright World Scientific Publishing Company. https://www.worldscientific.com/doi/abs/10.1142/S0129054118410058We consider an online model where an adversary constructs a set of 2s instances S instead of one single instance. The algorithm knows S and the adversary will choose one instance from S at random to present to the algorithm. We further focus on adversaries that construct sets of k-chromatic instances. In this setting, we provide upper and lower bounds on the competitive ratio for the online graph coloring problem as a function of the parameters in this model. Both bounds are linear in s and matching upper and lower bound are given for a specific set of algorithms that we call “minimalistic online algorithms”.Peer ReviewedPostprint (author's final draft

Online coloring problem with a randomized adversary and infinite advice

Online problems are those in which the instance is not given as a whole but by parts named requests. They arrise naturaly in computer science. Several examples are given such as ski rental problem, the server problem and the coloring problem. The performance of the online algorithms is analized in terms of the ratio between the cost of the algorithm and the cost of the optimal offline. This ratio is called the competitive ratio. Several models of online algorithms are described. They are deterministic algorithms, randomized algorithms and algorithms with advice. We present several upper and lower bounds for the competitive ratio in a particular case of the k-server problem. We review the known bounds for the coloring problem in the diferent models. We present a new model, the randomized adversary. For this model we present an upper bound and a restricted lower bound. Finally we conjecture an unrestricted lower bound and we present several approaches to the result

On the topology of 3 -manifolds and the Poincaré Conjecture

This work tries to understand the Poincaré conjecture statement and some of the tools that were used and developed towards its proving. It contains a historic introduction that explains how the problem arised and when the main steps towards its solving were maid. then there is a chapter that explains previous concepts about manifolds. In the following chapter the concepts of fundamental group and covering space are defined and also some propositions, theorems and examples of this concepts are provided. Finally there is a homology chapter with the definition of this concepts and some theorems and examples. And, to end with, a descriptive chapter about further developments in the conjectureLa conjectura de Poincaré sobre la caracterització topològica de l'esfera és actualment un teorema. El treball analitzarà la conjectura i els elements principals que intervenen en la seva demostració

Online graph coloring against a randomized adversary

Electronic version of an article published as Online graph coloring against a randomized adversary. "International journal of foundations of computer science", 1 Juny 2018, vol. 29, núm. 4, p. 551-569. DOI:10.1142/S0129054118410058 © 2018 copyright World Scientific Publishing Company. https://www.worldscientific.com/doi/abs/10.1142/S0129054118410058We consider an online model where an adversary constructs a set of 2s instances S instead of one single instance. The algorithm knows S and the adversary will choose one instance from S at random to present to the algorithm. We further focus on adversaries that construct sets of k-chromatic instances. In this setting, we provide upper and lower bounds on the competitive ratio for the online graph coloring problem as a function of the parameters in this model. Both bounds are linear in s and matching upper and lower bound are given for a specific set of algorithms that we call “minimalistic online algorithms”.Peer Reviewe