Quantum Computation, Markov Chains and Combinatorial Optimisation

This thesis addresses two questions related to the title, Quantum Computation, Markov Chains and Combinatorial Optimisation. The first question involves an algorithmic primitive of quantum computation, quantum walks on graphs, and its relation to Markov Chains. Quantum walks have been shown in certain cases to mix faster than their classical counterparts. Lifted Markov chains, consisting of a Markov chain on an extended state space which is projected back down to the original state space, also show considerable speedups in mixing time. We design a lifted Markov chain that in some sense simulates any quantum walk. Concretely, we construct a lifted Markov chain on a connected graph G with n vertices that mixes exactly to the average mixing distribution of a quantum walk on G. Moreover, the mixing time of this chain is the diameter of G. We then consider practical consequences of this result. In the second part of this thesis we address a classic unsolved problem in combinatorial optimisation, graph isomorphism. A theorem of Kozen states that two graphs on n vertices are isomorphic if and only if there is a clique of size n in the weak modular product of the two graphs. Furthermore, a straightforward corollary of this theorem and Lovász’s sandwich theorem is if the weak modular product between two graphs is perfect, then checking if the graphs are isomorphic is polynomial in n. We enumerate the necessary and sufficient conditions for the weak modular product of two simple graphs to be perfect. Interesting cases include complete multipartite graphs and disjoint unions of cliques. We find that all perfect weak modular products have factors that fall into classes of graphs for which testing isomorphism is already known to be polynomial in the number of vertices. Open questions and further research directions are discussed

Commutative Algorithms Approximate the LLL-distribution

Following the groundbreaking Moser-Tardos algorithm for the Lovasz Local Lemma (LLL), a series of works have exploited a key ingredient of the original analysis, the witness tree lemma, in order to: derive deterministic, parallel and distributed algorithms for the LLL, to estimate the entropy of the output distribution, to partially avoid bad events, to deal with super-polynomially many bad events, and even to devise new algorithmic frameworks. Meanwhile, a parallel line of work, has established tools for analyzing stochastic local search algorithms motivated by the LLL that do not fall within the Moser-Tardos framework. Unfortunately, the aforementioned results do not transfer to these more general settings. Mainly, this is because the witness tree lemma, provably, no longer holds. Here we prove that for commutative algorithms, a class recently introduced by Kolmogorov and which captures the vast majority of LLL applications, the witness tree lemma does hold. Armed with this fact, we extend the main result of Haeupler, Saha, and Srinivasan to commutative algorithms, establishing that the output of such algorithms well-approximates the LLL-distribution, i.e., the distribution obtained by conditioning on all bad events being avoided, and give several new applications. For example, we show that the recent algorithm of Molloy for list coloring number of sparse, triangle-free graphs can output exponential many list colorings of the input graph

LIPIcs

The Lovász Local Lemma (LLL) is a powerful tool in probabilistic combinatorics which can be used to establish the existence of objects that satisfy certain properties. The breakthrough paper of Moser and Tardos and follow-up works revealed that the LLL has intimate connections with a class of stochastic local search algorithms for finding such desirable objects. In particular, it can be seen as a sufficient condition for this type of algorithms to converge fast. Besides conditions for existence of and fast convergence to desirable objects, one may naturally ask further questions regarding properties of these algorithms. For instance, "are they parallelizable?", "how many solutions can they output?", "what is the expected "weight" of a solution?", etc. These questions and more have been answered for a class of LLL-inspired algorithms called commutative. In this paper we introduce a new, very natural and more general notion of commutativity (essentially matrix commutativity) which allows us to show a number of new refined properties of LLL-inspired local search algorithms with significantly simpler proofs

Cover and Hitting Times of Hyperbolic Random Graphs

We study random walks on the giant component of Hyperbolic Random Graphs (HRGs), in the regime when the degree distribution obeys a power law with exponent in the range (2, 3). In particular, we focus on the expected times for a random walk to hit a given vertex or visit, i.e. cover, all vertices. We show that up to multiplicative constants: the cover time is n(log n) 2 , the maximum hitting time is n log n, and the average hitting time is n. The first two results hold in expectation and a.a.s. and the last in expectation (with respect to the HRG). We prove these results by determining the effective resistance either between an average vertex and the well-connected “center” of HRGs or between an appropriately chosen collection of extremal vertices. We bound the effective resistance by the energy dissipated by carefully designed network flows associated to a tiling of the hyperbolic plane on which we overlay a forest-like structure

On the Study of Fitness Landscapes and the Max-Cut Problem

The goal of this thesis is to study the complexity of NP-Hard problems, using the Max-Cut and the Max-k-Cut problems, and the study of fitness landscapes. The Max-Cut and Max-k-Cut problems are well studied NP-hard problems specially since the approximation algorithm of Goemans and Williamson (1995) which introduced the use of SDP to solve relaxed problems. In order to prove the existence of a performance guarantee, the rounding step from the SDP solution to a Max-Cut solution is simple and randomized. For the Max-k-Cut problem, there exist several approximation algorithms but many of them have been proved to be equivalent. Similarly as in Max-Cut, these approximation algorithms use a simple randomized rounding to be able to get a performance guarantee. Ignoring for now the performance guarantee, one could ask if there is a rounding process that takes into account the structure of the relaxed solution since it is the result of an optimization problem. In this thesis we answered this question positively by using clustering as a rounding method. In order to compare the performance of both algorithms, a series of experiments were performed using the so-called G-set benchmark for the Max-Cut problem and using the Random Graph Benchmark of Goemans1995 for the Max-k-Cut problem. With this new rounding, larger cut values are found both for the Max-Cut and the Max-k-Cut problems, and always above the value of the performance guarantee of the approximation algorithm. This suggests that taking into account the structure of the problem to design algorithms can lead to better results, possibly at the cost of a worse performance guarantee. An example for the vertex k-center problem can be seen in Garcia-Diaz et al. (2017), where a 3-approximation algorithm performs better than a 2-approximation algorithm despite having a worse performance guarantee. Landscapes over discrete configurations spaces are an important model in evolutionary and structural biology, as well as many other areas of science, from the physics of disordered systems to operations research. A landscape is a function defined on a very large discrete set V that carries an additional metric or at least topological structure into the real numbers R. We will consider landscapes defined on the vertex set of undirected graphs. Thus let G=G(V,E) be an undirected graph and f an arbitrary real-valued function taking values from V . We will refer to the triple (V,E,f) as a landscape over G. We say two configurations x,y in V are neutral if f(x)=f(y). We colloquially refer to a landscape as 'neutral'' if a substantial fraction of adjacent pairs of configurations are neutral. A flat landscape is one where f is constant. The opposite of flatness is ruggedness and it is defined as the number of local optima or by means of pair correlation functions. These two key features of a landscape, ruggedness and neutrality, appear to be two sides of the same coin. Ruggedness can be measured either by correlation properties, which are sensitive to monotonic transformation of the landscape, and by combinatorial properties such as the lengths of downhill paths and the number of local optima, which are invariant under monotonic transformations. The connection between the two views has remained largely unexplored and poorly understood. For this thesis, a survey on fitness landscapes is presented, together with the first steps in the direction to find this connection together with a relation between the covariance matrix of a random landscape model and its ruggedness

Algorithms for Fundamental Problems in Computer Networks.

Traditional studies of algorithms consider the sequential setting, where the whole input data is fed into a single device that computes the solution. Today, the network, such as the Internet, contains of a vast amount of information. The overhead of aggregating all the information into a single device is too expensive, so a distributed approach to solve the problem is often preferable. In this thesis, we aim to develop efficient algorithms for the following fundamental graph problems that arise in networks, in both sequential and distributed settings. Graph coloring is a basic symmetry breaking problem in distributed computing. Each node is to be assigned a color such that adjacent nodes are assigned different colors. Both the efficiency and the quality of coloring are important measures of an algorithm. One of our main contributions is providing tools for obtaining colorings of good quality whose existence are non-trivial. We also consider other optimization problems in the distributed setting. For example, we investigate efficient methods for identifying the connectivity as well as the bottleneck edges in a distributed network. Our approximation algorithm is almost-tight in the sense that the running time matches the known lower bound up to a poly-logarithmic factor. For another example, we model how the task allocation can be done in ant colonies, when the ants may have different capabilities in doing different tasks. The matching problems are one of the classic combinatorial optimization problems. We study the weighted matching problems in the sequential setting. We give a new scaling algorithm for finding the maximum weight perfect matching in general graphs, which improves the long-standing Gabow-Tarjan's algorithm (1991) and matches the running time of the best weighted bipartite perfect matching algorithm (Gabow and Tarjan, 1989). Furthermore, for the maximum weight matching problem in bipartite graphs, we give a faster scaling algorithm whose running time is faster than Gabow and Tarjan's weighted bipartite {it perfect} matching algorithm.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113540/1/hsinhao_1.pd