Computing the Stretch of an Embedded Graph

ABSTRACTAn independent set of size in a finite undirected graph is a set of vertices of the graph, no two of which are connected by an edge. Let be the number of independent sets of size in the graph and let . In 1987, Alavi, Malde, Schwenk, and Erdős asked if the independent set sequence of a tree is unimodal (the sequence goes up and then down). This problem is still open. In 2006, Levit and Mandrescu showed that the last third of the independent set sequence of a tree is decreasing. We show that the first 46.8% of the independent set sequence of a random tree is increasing with (exponentially) high probability as the number of vertices goes to infinity. So, the question of Alavi, Malde, Schwenk, and Erdős is “four‐fifths true,” with high probability. We also show unimodality of the independent set sequence of Erdős–Rényi random graphs, when the expected degree of a single vertex is large (with [exponentially] high probability as the number of vertices in the graph goes to infinity, except for a small region near the mode). A weaker result is shown for random regular graphs. The structure of independent sets of size as varies is of interest in probability, statistical physics, combinatorics, and computer science.

Fully Dynamic Algorithms for Maintaining Shortest Paths Trees

We present an algorithm which calculates a minimum cut and its weight in an undirected graph with nonnegative real edge weights, n vertices and m edges, in time O(max(log n, min(m/n,δG/e)) n2), where e is the minimal edge weight, and δG is the minimal weighted degree. For integer edge weights this time is further improved to O(δG n2) and O(λG n2). In both cases these bounds are improvements of the previously known best bounds of deterministic algorithms. These were O(nm + n2 log n) for real edge weights and O(nM + n2) and O(M + λG n2) for integer weights, where M is the sum of all edge weights.

We study paths of time-length $t$ of a continuous-time random walk on $\mathbb{Z}^{2}$ subject to self-interaction that depends on the geometry of the walk range and a collection of random, uniformly positive and finite edge weights. The interaction enters through a Gibbs weight at inverse temperature $\beta$; the “energy” is the total sum of the edge weights for edges on the outer boundary of the range. For edge weights sampled from a translation-invariant, ergodic law, we prove that the range boundary condensates around an asymptotic shape in the limit $t\to\infty$ followed by $\beta\to\infty$. The limit shape is a minimizer (unique, modulo translates) of the sum of the principal harmonic frequency of the domain and the perimeter with respect to the first-passage percolation norm derived from (the law of) the edge weights. A dense subset of all norms in $\mathbb{R}^{2}$, and thus a large variety of shapes, arise from the class of weight distributions to which our proofs apply.

Special embeddings of Cayley diagrams

Self-avoiding walks interacting with a surface are a useful model of polymer adsorption and one can choose to weight the walk according to the number of vertices or the number of edges in the surface. We consider a combined model of self-avoiding walks on the d-dimensional hypercubic lattice with weights associated with the numbers of vertices and edges in the surface and investigate its phase diagram. We also derive inequalities relating the critical points in the edge and vertex weighting schemes.

A linear time algorithm for the maximum capacity path problem

We consider optimization problems on weighted graphs where vertex and edge weights are polynomial functions of a parameter λ. We show that, if a problem satisfies certain regularity properties and the underlying graph has bounded tree-width, the number of changes in the optimum solution is polynomially bounded. We also show that the description of the sequence of optimum solutions can be constructed in polynomial time and that certain parametric search problems can be solved in O(n log n) time, where n is the number of vertices in the graph.KeywordsComposition OperatorSteiner TreeParse TreeLower EnvelopeOuterplanar GraphThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work studies the problem of 2-dimensional searching for the 3-sided range query of the form [a, b] x (-∞, c] in both main and external memory, by considering a variety of input distributions. A dynamic linear main memory solution is proposed, which answers 3-sided queries in O(log n + t) worst case time and scales with O (log log n) expected with high probability update time, under continuous μ-random distributions of the x and y coordinates, where n is the current number of stored points and t is the size of the query output. Our expected update bound constitutes a considerable improvement over the O(log n) update time bound achieved by the classic Priority Search Tree of McCreight [23], as well as over the Fusion Priority Search Tree of Willard [30], which requires O(log n/log log n) time for all operations. Moreover, we externalize this solution, gaining O(logBn + t/B) worst case and O(logBlogn) amortized expected with high probability I/Os for query and update operations respectively, where B is the disk block size. Then, combining the Modified Priority Search Tree [27] with the Priority Search Tree [23], we achieve a query time of O(log log n + t) expected with high probability and an update time of O(log log n) expected with high probability, under the assumption that the x-coordinates are continuously drawn from a smooth distribution and the y-coordinates are continuously drawn from a more restricted class of distributions. The total space is linear. Finally, we externalize this solution, obtaining a dynamic data structure that answers 3-sided queries in O(logB log n + t/B) I/Os expected with high probability, and it can be updated in O(logB log n) I/Os amortized expected with high probability and consumes O(n/B) space, under the same assumptions.

Consider a complete weighted bipartite graph G in which each left vertex u has two real numbers intercept and slope, each right vertex v has a real number quality, and the weight of any edge (u, v) is defined as the intercept of u plus the slope of u times the quality of v. Let m (resp., n) denote the number of left (resp., right) vertices, and assume that m geq n. We develop a fast algorithm for computing a maximum weight matching (MWM) of such a graph. Our algorithm begins by computing an MWM of the subgraph induced by the n right vertices and an arbitrary subset of n left vertices; this step is straightforward to perform in O(n log n) time. The remaining m - n left vertices are then inserted into the graph one at a time, in arbitrary order. As each left vertex is inserted, the MWM is updated. It is relatively straightforward to process each such insertion in O(n) time; our main technical contribution is to improve this time bound to O(sqrt{n} log^2 n). This result has an application related to unit-demand auctions. It is well known that the VCG mechanism yields a suitable solution (allocation and prices) for any unit-demand auction. The graph G may be viewed as encoding a special kind of unit-demand auction in which each left vertex u represents a unit-demand bid, each right vertex v represents an item, and the weight of an edge (u, v) represents the offer of bid u on item v. In this context, our fast insertion algorithm immediately provides an O(sqrt{n} log^2 n)-time algorithm for updating a VCG allocation when a new bid is received. We show how to generalize the insertion algorithm to update (an efficient representation of) the VCG prices within the same time bound.

We consider the problem of bounding the complexity of the ofn surface patches in 3-space, all algebraic of constant maximum degree, and bounded by algebraic arcs of constant maximum degree, with the additional property that the interiors of any triple of these surfaces intersect in at most two points. We show that the number of vertices on the ofn such surface patches is $$O(n^2 \cdot 2^{c\sqrt {\log n} } )$$ , for some constantc depending on the shape and degree of the surface patches. We apply this result to obtain an upper bound on the combinatorial complexity of the lower envelope of the space of allrays in 3-space that lie above a given polyhedral terrainK withn edges. This consists of all rays that touch the terrain (but otherwise lie above it). We show that the combinatorial complexity of this ray-envelope is $$O(n^3 \cdot 2^{c\sqrt {\log n} } )$$ for some constantc; in particular, there are at most that many rays that pass above the terrain and touch it in four edges. This bound, combined with the analysis of de Berget al. [4], gives an upper bound (which is almost tight in the worst case) on the number of topologically different orthographic views of such a terrain.

We show that the number of vertices, edges, and faces of the union of k convex polyhedra in 3-space, having a total of n faces, is O(k3 + kn log k). This bound is almost tight in the worst case, as there exist collections of polyhedra with $\Omega(k^3+kn\alpha(k))$ union complexity. We also describe a rather simple randomized incremental algorithm for computing the boundary of the union in O(k3 + kn log k log n) expected time.

Given a quadrilateral ABCD in \(K_n\) and the distances of edges, the special frequency quadrilaterals are derived as two of the three sum distances \(d(A,B)\,+\,d(C,D)\), \(d(A,C)\,+\,d(B,D)\), and \(d(A,D)\,+\,d(B,C)\) are equal. A probability model formulated based on the special frequency quadrilaterals implies the edges in the optimal Hamiltonian cycle are different from the other edges in \(K_n\). Christofides proposed a \(\frac{3}{2}\)-approximation algorithm for metric traveling salesman problem (TSP) that runs in \(O(n^3)\) time. Cornuejols and Nemhauser constructed a family of graphs where the performance ratio of Christofides algorithm is exactly \(\frac{3}{2}\) in the worst case. We apply the special frequency quadrilaterals to the family of metric TSP instances for cutting the useless edges. In the end, the complex graph is reduced to a simple graph where the optimal Hamiltonian cycle can be detected in O(n) time, where \(n\ge 4\) is the number of vertices in the graph.

The restricted rotation distance d R (S, T) between two binary trees S, T of n vertices is the minimum number of rotations by which S can be transformed into T, where rotations can only take place at the root of the tree, or at the right child of the root. A sharp upper bound d R (S, T) ≤ 4n – 8 is known, based on the word metric of Thompson’s group. We refine this bound to a sharp d R (S, T) ≤ 4n – 8 – ρS – ρT, where ρS and ρT are the numbers of vertices in the rightmost vertex chains of the two trees, by means of a very simple transformation algorithm based on elementary properties of trees. We then generalize the concept of restricted rotation to k-restricted rotation, by allowing rotations to take place at all the vertices of the highest k levels of the tree. For k = 2 we show that not much is gained in the worst case, although the classical problem of rebalancing an AVL tree can be solved efficiently, in particular rebalancing after vertex deletion requires O(log n) rotations as in the standard algorithm. Finding significant bounds and applications for k ≥ 3 is open.

Structure of edges in plane graphs with bounded dual edge weight