Matching is one of the most fundamental algorithmic graph problems. Many variants of matching problems have been studied on different classes of graphs, the one of special interest to us being the Maximum Cardinality Bipartite Matching in Planar Graphs. In this work, we present a novel sparsification based approach for computing maximum/perfect bipartite matching in planar graphs. The overall complexity of our algorithm is O(n^6/5 log² n) where n is the number of vertices in the graph, bettering the O(n^3/2) time achieved independently by Hopcroft-Karp algorithm and by Lipton and Tarjan divide and conquer approach using planar separators. Our algorithm combines the best of both these standard algorithms along with our sparsification technique and rich planar graph properties to achieve the speed up. Our algorithm is not the fastest, with the existence of O(n log³ n) algorithm based on max-flow reduction. / MS / A matching in a graph can be defined as a subset of edges without common vertices. A matching algorithm finds a maximum set of such vertex-disjoint edges. Many real life resource allocation problems can be solved efficiently by modelling them as a matching problem. While many variants of matching problems have been studied on different classes of graphs, the simplest and the most popular among them is the Maximum Cardinality Bipartite Matching problem. Bipartite matching arises in varied applications like matching applicants to job openings, matching ads to user queries, matching threads to tasks in OS scheduler, matching protein sequences based on their structures and so on. In this work, we present an efficient algorithm for computing maximum cardinality bipartite matching in planar graphs. Planar graphs are sparse graphs and have interesting structural properties which allow us to design faster algorithms in planar setting for problems that are otherwise considered hard in arbitrary graphs. We use a new sparsification based approach where we maintain a compact and accurate representation of the original graph with a lesser number of vertices. Our algorithm combines the features of the best known bipartite matching algorithm for an arbitrary graph with the novel sparsification approach to achieve the speedup.

A coloring of a graph is an assignment of colors to its vertices so that no two adjacent vertices are given the same color. The chromatic number of a graph is the least number of colors needed to color all of its vertices. Graph coloring problems can be applied to many real world applications, such as scheduling and register allocation. Computationally, the decision problem of whether a general graph is m-colorable is NP-complete for m ≥ 3. The graph studied in this thesis is a well-known combinatorial object, the k-dimensional hypercube, Qk. The hypercube itself is 2-colorable for all k; however, coloring the square of the cube is a much more interesting problem. This is the graph in which the vertices are binary vectors of length k, and two vertices are adjacent if and only if the Hamming distance between the two vectors is at most 2. Any color class in a coloring of Q2k is a binary (k;M, 3) code. This thesis will begin with an introduction to binary codes and their structure. One of the most fundamental combinatorial problems is finding optimal binary codes, that is, binary codes with the maximum cardinality satisfying a specified length and minimum distance. Many upper and lower bounds have been produced, and we will analyze and apply several of these. This leads to many interesting results about the chromatic number of the square of the cube. The smallest k for which the chromatic number of Q2k is unknown is k = 8; however, it can be determined that this value is either 13 or 14. Computational approaches to determine the chromatic number of Q28 were performed. We were unable to determine whether 13 or 14 is the true value; however, much valuable insight was learned about the structure of this graph and the computational difficulty that lies within. Since a 13-coloring of Q28 must have between 9 and 12 color classes being (8; 20; 3) binary codes, this led to a thorough investigation of the structure of such binary codes.

A coloring of a graph is an assignment of colors to its vertices so that no two adjacent vertices are given the same color. The chromatic number of a graph is the least number of colors needed to color all of its vertices. Graph coloring problems can be applied to many real world applications, such as scheduling and register allocation. Computationally, the decision problem of whether a general graph is m-colorable is NP-complete for m ≥ 3. The graph studied in this thesis is a well-known combinatorial object, the k-dimensional hypercube, Qk. The hypercube itself is 2-colorable for all k; however, coloring the square of the cube is a much more interesting problem. This is the graph in which the vertices are binary vectors of length k, and two vertices are adjacent if and only if the Hamming distance between the two vectors is at most 2. Any color class in a coloring of Q2k is a binary (k;M, 3) code. This thesis will begin with an introduction to binary codes and their structure. One of the most fundamental combinatorial problems is finding optimal binary codes, that is, binary codes with the maximum cardinality satisfying a specified length and minimum distance. Many upper and lower bounds have been produced, and we will analyze and apply several of these. This leads to many interesting results about the chromatic number of the square of the cube. The smallest k for which the chromatic number of Q2k is unknown is k = 8; however, it can be determined that this value is either 13 or 14. Computational approaches to determine the chromatic number of Q28 were performed. We were unable to determine whether 13 or 14 is the true value; however, much valuable insight was learned about the structure of this graph and the computational difficulty that lies within. Since a 13-coloring of Q28 must have between 9 and 12 color classes being (8; 20; 3) binary codes, this led to a thorough investigation of the structure of such binary codes.

A coloring of a graph is an assignment of colors to its vertices so that no two adjacent vertices are given the same color. The chromatic number of a graph is the least number of colors needed to color all of its vertices. Graph coloring problems can be applied to many real world applications, such as scheduling and register allocation. Computationally, the decision problem of whether a general graph is m-colorable is NP-complete for m ≥ 3. The graph studied in this thesis is a well-known combinatorial object, the k-dimensional hypercube, Qk. The hypercube itself is 2-colorable for all k; however, coloring the square of the cube is a much more interesting problem. This is the graph in which the vertices are binary vectors of length k, and two vertices are adjacent if and only if the Hamming distance between the two vectors is at most 2. Any color class in a coloring of Q2k is a binary (k;M, 3) code. This thesis will begin with an introduction to binary codes and their structure. One of the most fundamental combinatorial problems is finding optimal binary codes, that is, binary codes with the maximum cardinality satisfying a specified length and minimum distance. Many upper and lower bounds have been produced, and we will analyze and apply several of these. This leads to many interesting results about the chromatic number of the square of the cube. The smallest k for which the chromatic number of Q2k is unknown is k = 8; however, it can be determined that this value is either 13 or 14. Computational approaches to determine the chromatic number of Q28 were performed. We were unable to determine whether 13 or 14 is the true value; however, much valuable insight was learned about the structure of this graph and the computational difficulty that lies within. Since a 13-coloring of Q28 must have between 9 and 12 color classes being (8; 20; 3) binary codes, this led to a thorough investigation of the structure of such binary codes. / Graduate Studies, College of (Okanagan) / Graduate