Single-Elimination Brackets Fail to Approximate Copeland Winner

Single-elimination (SE) brackets appear commonly in both sports tournaments and the voting theory literature. In certain tournament models, they have been shown to select the unambiguously-strongest competitor with optimum probability. By contrast, we reevaluate SE brackets through the lens of approximation, where the goal is to select a winner who would beat the most other competitors in a round robin (i.e., maximize the Copeland score), and find them lacking. Our primary result establishes the approximation ratio of a randomly-seeded SE bracket is 2^{- Theta(sqrt{log n})}; this is underwhelming considering a 1/2 ratio is achieved by choosing a winner uniformly at random. We also establish that a generalized version of the SE bracket performs nearly as poorly, with an approximation ratio of 2^{- Omega(sqrt[4]{log n})}, addressing a decade-old open question in the voting tree literature

When You Come at the King You Best Not Miss

A tournament is an orientation of a complete graph. We say that a vertex x in a tournament T controls another vertex y if there exists a directed path of length at most two from x to y. A vertex is called a king if it controls every vertex of the tournament. It is well known that every tournament has a king. We follow Shen, Sheng, and Wu [Jian Shen et al., 2003] in investigating the query complexity of finding a king, that is, the number of arcs in T one has to know in order to surely identify at least one vertex as a king. The aforementioned authors showed that one always has to query at least ?(n^{4/3}) arcs and provided a strategy that queries at most O(n^{3/2}). While this upper bound has not yet been improved for the original problem, [Biswas et al., 2017] proved that with O(n^{4/3}) queries one can identify a semi-king, meaning a vertex which controls at least half of all vertices. Our contribution is a novel strategy which improves upon the number of controlled vertices: using O(n^{4/3} polylog n) queries, we can identify a (1/2+2/17)-king. To achieve this goal we use a novel structural result for tournaments

Randomized and quantum query complexities of finding a king in a tournament

A tournament is a complete directed graph. It is well known that every tournament contains at least one vertex v such that every other vertex is reachable from v by a path of length at most 2. All such vertices v are called *kings* of the underlying tournament. Despite active recent research in the area, the best-known upper and lower bounds on the deterministic query complexity (with query access to directions of edges) of finding a king in a tournament on n vertices are from over 20 years ago, and the bounds do not match: the best-known lower bound is Omega(n^{4/3}) and the best-known upper bound is O(n^{3/2}) [Shen, Sheng, Wu, SICOMP'03]. Our contribution is to show essentially *tight* bounds (up to logarithmic factors) of Theta(n) and Theta(sqrt{n}) in the *randomized* and *quantum* query models, respectively. We also study the randomized and quantum query complexities of finding a maximum out-degree vertex in a tournament

Processing massive graphs under limited visibility

Graphs are one of the most important and widely used combinatorial structures in mathematics. Their ability to model many real world scenarios which involve a large network of related entities make them useful across disciplines. They are useful as an abstraction in the analysis of networked structures such as the Internet, social networks, road networks, biological networks and many more. The graphs arising out of many real world phenomenon can be very large and they keep evolving over time. For example, Facebook reported over 2:9 billion monthly active users in 2022. Another very large and dynamic network is the human brain consisting of around 1011 nodes and many more edges. These large and evolving graphs present new challenges for algorithm designers. Traditional graph algorithms designed to work with centralised and sequential computing models are rendered useless due to their prohibitively high resource usage. In fact one needs huge amounts of resources just to read the entire graph. A number of new theoretical models have been devised over the years to keep up with the trends in the modern computing systems capable of handing massive input datasets. Some of these models such as streaming model and the query model allow the algorithm to view the graph piecemeal. In some cases, the model allows the graph to be processed by a set of interconnected computing elements such as in distributed computing. In this thesis we address some graph problems in these non-centralised, non-sequential models of computing with a limited access to the input graph. Specifically, we address three different graph problems, each in a different computing model. The first problem we look at is the computation of approximate shortest paths in dynamic streams. The second problem deals with finding kings in tournament graphs, given query access to the arcs of the tournament. The third and the final problem we investigate is a local test criteria for testing the expansion of a graph in the distributed CONGEST model

Query Complexity of Tournament Solutions

A directed graph where there is exactly one edge between every pair of vertices is called a tournament. Finding the “best” set of vertices of a tournament is a well studied problem in social choice theory. A tournament solution takes a tournamentas input and outputs a subset of vertices of the input tournament. However, in many applications, for example, choosing the best set of drugs from a given set of drugs, the edges of the tournament are given only implicitly and knowing the orientation of an edge is costly. In such scenarios, we would like to know the best set of vertices (according to some tournament solution) by “querying” as few edges as possible. We, in this paper, precisely study this problem for commonly used tournament solutions: given an oracle access to the edges of a tournament T , find f(T) by querying as few edges as possible, for a tournament solution f. We first show that the set of Condorcet non-losers in a tournament can be found by querying 2n−⌊log n⌋−2 edges only and this is tight in the sense that every algorithm for finding the set of Condorcet non-losers needs to query at least 2n−⌊log n⌋−2 edges in the worst case, where n is the number of vertices in the input tournament. We then move on to study other popular tournament solutions and show that any algorithm for finding the Copeland set, the Slater set, the Markov set, the bipartisan set, the uncovered set, the Banks set, and the top cycle must query Ω(n2) edges in the worst case. On the positive side, we are able to circumvent our strong query complexity lower bound results by proving that, if the size of the top cycle of the input tournament is at most k, then we can find all the tournament solutions mentioned above by querying O(nk + n log n / log(1− 1 / k ) ) edges only