Polütoopide laienditega seotud ülesanded

Väitekirja elektrooniline versioon ei sisalda publikatsiooneLineaarplaneerimine on optimeerimine matemaatilise mudeliga, mille sihi¬funktsioon ja kitsendused on esitatud lineaarsete seostega. Paljusid igapäeva elu väljakutseid võime vaadelda lineaarplaneerimise vormis, näiteks miinimumhinna või maksimaalse tulu leidmist. Sisepunkti meetod saavutab häid tulemusi nii teoorias kui ka praktikas ning lahendite leidmise tööaeg ja lineaarsete seoste arv on polünomiaalses seoses. Sellest tulenevalt eksponentsiaalne arv lineaarseid seoseid väljendub ka ekponentsiaalses tööajas. Iga vajalik lineaarne seos vastab ühele polütoobi P tahule, mis omakorda tähistab lahendite hulka. Üks võimalus tööaja vähendamiseks on suurendada dimensiooni, mille tulemusel väheneks ka polütoobi tahkude arv. Saadud polütoopi Q nimeta¬takse polütoobi P laiendiks kõrgemas dimensioonis ning polütoobi Q minimaalset tahkude arvu nimetakakse polütoobi P laiendi keerukuseks, sellisel juhul optimaalsete lahendite hulk ei muutu. Tekib küsimus, millisel juhul on võimalik leida laiend Q, mille korral tahkude arv on polünomiaalne. Mittedeterministlik suhtluskeerukus mängib olulist rolli tõestamaks polütoopide laiendite keerukuse alampiiri. Polütoobile P vastava suhtluskeerukuse leidmine ning alamtõkke tõestamine väistavad võimalused leida laiend Q, mis ei oleks eksponentsiaalne. Käesolevas töös keskendume me juhuslikele Boole'i funktsioonidele f, mille tihedusfunktsioon on p = p(n). Me pakume välja vähima ülemtõkke ning suurima alamtõkke mittedeterministliku suhtluskeerukuse jaoks. Lisaks uurime me ka pedigree polütoobi graafi. Pedigree polütoop on rändkaupmehe ülesande polütoobi laiend, millel on kombinatoorne struktuur. Polütoobi graafi võib vaadelda kui abstraktset graafi ning see annab informatsiooni polütoobi omaduste kohta.The linear programming (LP for short) is a method for finding an optimal solution, such as minimum cost or maximum profit for a linear function subject to linear constraints. But having an exponential number of inequalities gives the exponential running time in solving linear program. A polytope, let's say P, represents the space of the feasible solution. One idea for decreasing the running time of the problem, is lifting the polytope P tho the higher dimensions with the goal of decresing the number of inequalities. The polytope in higher dimension, let's say Q, is the extension of the original polytope P and the minimum number of facets that Q can have is the extension complexity of P. Then the optimal solution of the problem over Q, gives the optimal solution over P. The natural question may raise is when is it possible to have an extension with a polynomial number of inequalities? Nondeterministic communication complexity is a powerful tool for proving lower bound on the extension complexity of a polytopes. Finding a suitable communication complexity problem corresponded to a polytope P and proving a linear lower bound for the nondeterministic communication complexity of it, will rule out all the attempts for finding sub-exponential size extension Q of P. In this thesis, we focus on the random Boolean functions f, with density p = p(n). We give tight upper and lower bounds for the nondeterministic communication complexity and parameters related to it. Also, we study the rank of fooling set matrix which is an important lower bound for nondeterministic communication complexity. Finally, we investigate the graph of the pedigree polytope. Pedigree polytope is an extension of TSP (traveling salesman problem; the most extensively studied problem in combinatorial optimization) polytopes with a nice combinatorial structure. The graph of a polytope can be regarded as an abstract graph and it reveals meaningful information about the properties of the polytope

On vertex adjacencies in the polytope of pyramidal tours with step-backs

We consider the traveling salesperson problem in a directed graph. The pyramidal tours with step-backs are a special class of Hamiltonian cycles for which the traveling salesperson problem is solved by dynamic programming in polynomial time. The polytope of pyramidal tours with step-backs $PSB (n)$ is defined as the convex hull of the characteristic vectors of all possible pyramidal tours with step-backs in a complete directed graph. The skeleton of $PSB (n)$ is the graph whose vertex set is the vertex set of $PSB (n)$ and the edge set is the set of geometric edges or one-dimensional faces of $PSB (n)$. The main result of the paper is a necessary and sufficient condition for vertex adjacencies in the skeleton of the polytope $PSB (n)$ that can be verified in polynomial time.Comment: in Englis

Алгоритмы поиска с возвратом для построения гамильтонова разложения 4-регулярного мультиграфа

We consider a Hamiltonian decomposition problem of partitioning a regular graph into edge-disjoint Hamiltonian cycles. It is known that verifying vertex non-adjacency in the 1-skeleton of the symmetric and asymmetric traveling salesperson polytopes is an NP-complete problem. On the other hand, a suffcient condition for two vertices to be non-adjacent can be formulated as a combinatorial problem of finding a Hamiltonian decomposition of a 4-regular multigraph. We present two backtracking algorithms for verifying vertex non-adjacency in the 1-skeleton of the traveling salesperson polytope and constructing a Hamiltonian decomposition: an algorithm based on a simple path extension and an algorithm based on the chain edge fixing procedure. Based on the results of the computational experiments for undirected multigraphs, both backtracking algorithms lost to the known heuristic general variable neighborhood search algorithm. However, for directed multigraphs, the algorithm based on chain fixing of edges showed comparable results with heuristics on instances with existing solutions, and better results on instances of the problem where the Hamiltonian decomposition does not exist.Рассматривается задача построения гамильтонова разложения регулярного мультиграфа на гамильтоновы циклы без общих рёбер. Известно, что проверка несмежности вершин в полиэдральных графах симметричного и асимметричного многогранников коммивояжёра является NP-полной задачей. С другой стороны, достаточное условие несмежности вершин можно сформулировать в виде комбинаторной задачи построения гамильтонова разложения 4-регулярного мультиграфа. В статье представлены два алгоритма поиска с возвратом для проверки несмежности вершин в полиэдральном графе коммивояжёра и построения гамильтонова разложения 4-регулярного мультиграфа: алгоритм на основе последовательного расширения простого пути и алгоритм на основе процедуры цепного фиксирования рёбер. По результатам вычислительных экспериментов для неориентированных мультиграфов оба переборных алгоритма проиграли известному эвристическому алгоритму поиска с переменными окрестностями. Однако для ориентированных мультиграфов алгоритм на основе цепного фиксирования рёбер показал сопоставимые результаты с эвристиками на экземплярах задачи, имеющих решение, и лучшие результаты на экземплярах задачи, для которых гамильтонова разложения не существует

Backtracking algorithms for constructing the Hamiltonian decomposition of a 4-regular multigraph

We consider a Hamiltonian decomposition problem of partitioning a regular multigraph into edge-disjoint Hamiltonian cycles. It is known that verifying vertex nonadjacency in the 1-skeleton of the symmetric and asymmetric traveling salesperson polytopes is an NP-complete problem. On the other hand, a sufficient condition for two vertices to be nonadjacent can be formulated as a combinatorial problem of finding a Hamiltonian decomposition of a 4-regular multigraph. We present two backtracking algorithms for verifying vertex nonadjacency in the 1-skeleton of the traveling salesperson polytope and constructing a Hamiltonian decomposition: an algorithm based on a simple path extension and an algorithm based on the chain edge fixing procedure. According to the results of computational experiments for undirected multigraphs, both backtracking algorithms lost to the known general variable neighborhood search algorithm. However, for directed multigraphs, the algorithm based on chain edge fixing showed comparable results with heuristics on instances with the existing solution and better results on instances of the problem where the Hamiltonian decomposition does not exist.Comment: In Russian. Computational experiments are revise

An Integer Programming approach to Bayesian Network Structure Learning

We study the problem of learning a Bayesian Network structure from data using an Integer Programming approach. We study the existing approaches, an in particular some recent works that formulate the problem as an Integer Programming model. By discussing some weaknesses of the existing approaches, we propose an alternative solution, based on a statistical sparsification of the search space. Results show how our approach can lead to promising results, especially for large network

Computing the Nucleolus of Matching and b-Matching Games

In the classical weighted matching problem the optimizer is given a graph with edge weights and their goal is to find a matching which maximizes the sum of the weights of edges in the matching. It is typically assumed in this process that the optimizer has unilateral control over the decision to take each edge. Where cooperative game theory intersects combinatorial optimization this assumption is subverted. In a cooperative matching game each vertex of the graph is controlled by a distinct player, and an edge can only be taken into a matching with the cooperation of the players at each of its vertices. One can think of the weight of an edge as representing the value the players of that edge generate by collaborating in partnership. In this setting the question is more than simply can we find an optimal matching, as in the classic matching problem, but also how should the players share the total value of the matching amongst themselves. The players should share the value they generate in a way that fairly respects the contributions of each player, and which encourages as well as possible the stable participation of every player in the network. Cooperative game theory formulates such fair distributions of wealth as solution concepts. One classical and beautiful solution concept is the nucleolus. Intuitively the nucleolus distributes value so that the worst off groups of players are as satisfied as possible, and subject to that the second worst off groups, and so on. Here we think of satisfaction as the difference between how much value the players were distributed versus how much they could have generated on their own had they seceded from the grand coalition. This thesis studies the nucleolus of matching games, and their generalization to b-matching games where each player can take on multiple partnerships simultaneously, from a computational perspective. We study when the nucleolus of a b-matching game can be computed efficiently and when it is intractable to do so. Chapter 2 describes an algorithm for computing the nucleolus of any weighted cooperative matching game in polynomial time. Chapter 3 studies the computational complexity of b-matching games. We show that computing the nucleolus of such games is NP-hard even when every vertex has b-value 3, the graph is unweighted, bipartite, and of maximum degree 7. Finally, in Chapter 4 we show that when the problem of determining the worst off coalition under a given allocation in a cooperative game can be formulated as a dynamic program then the nucleolus of the game can be computed in time which is only a polynomial factor larger than the time it takes to solve said dynamic program. We apply this result to show that nucleolus of b-matching games can be computed in polynomial time on graphs of bounded treewidth

Foundations of Quantum Theory: From Classical Concepts to Operator Algebras

Quantum physics; Mathematical physics; Matrix theory; Algebr