An Eulerian circuit in a directed graph is one of the most fundamental Graph Theory notions. Detecting if a graph G has a unique Eulerian circuit can be done in polynomial time via the BEST theorem by de Bruijn, van Aardenne-Ehrenfest, Smith and Tutte (1941–1951) [15], [16] (involving counting arborescences), or via a tailored characterization by Pevzner, 1989 (involving computing the intersection graph of simple cycles of G), both of which thus rely on overly complex notions for the simpler uniqueness problem. In this paper we give a new linear-time checkable characterization of directed graphs with a unique Eulerian circuit. This is based on a simple condition of when two edges must appear consecutively in all Eulerian circuits, in terms of cut nodes of the underlying undirected graph of G. As a by-product, we can also compute in linear-time all maximal safe walks appearing in all Eulerian circuits, for which Nagarajan and Pop proposed in 2009 [12] a polynomial-time algorithm based on Pevzner characterization.

Greedy BST (or simply Greedy) is an online self-adjusting binary search tree defined in the geometric view ([Lucas, 1988; Munro, 2000; Demaine, Harmon, Iacono, Kane, Patrascu, SODA 2009). Along with Splay trees (Sleator, Tarjan 1985), Greedy is considered the most promising candidate for being dynamically optimal, i.e., starting with any initial tree, their access costs on any sequence is conjectured to be within O(1) factor of the offline optimal. However, despite having received a lot of attention in the past four decades, the question has remained elusive even for highly restricted input. In this paper, we prove new bounds on the cost of Greedy in the “pattern avoidance” regime. Our new results include: • The (preorder) traversal conjecture for Greedy holds up to a factor of O(2α(n)), improving upon the bound of 2α(n)O(1) in (Chalermsook et al., FOCS 2015) where α(n) is the inverse Ackermann function of n. This is the best known bound obtained by any online BSTs. • We settle the postorder traversal conjecture for Greedy. Previously this was shown for Splay trees only in certain special cases (Levy and Tarjan, WADS 2019). • The deque conjecture for Greedy holds up to a factor of O(α(n)), improving upon the bound 2O(α(n)) in (Chalermsook, et al., WADS 2015). This is arguably “one step away” from the bound O(α*(n)) for Splay trees (Pettie, SODA 2010). • The split conjecture holds for Greedy up to a factor of O(2α(n)). Previously the factor of O(α(n)) was shown for Splay trees only in a special case (Lucas, 1988). The input sequences in traversal and deque conjectures are perhaps “easiest” in the pattern-avoiding input classes and yet among the most notorious special cases of the dynamic optimality conjecture. Key to all these results is to partition (based on the input structures) the execution log of Greedy into several simpler-to-analyze subsets for which classical forbidden submatrix bounds can be leveraged. We believe that this simple method will find further applications in doing amortized analysis of data structures via extremal combinatorics. Finally, we show the applicability of this technique to handle a class of increasingly complex pattern-avoiding input sequences, called k-increasing sequences. As a bonus, we discover a new class of permutation matrices whose extremal bounds are polynomially bounded. This gives a partial progress on an open question by Jacob Fox (2013). * The full version of the paper can be accessed at https://arxiv.org/abs/2211.04112

In this article, we consider the following problem. Given a directed graph G , output all walks of G that are sub-walks of all closed edge-covering walks of G . This problem was first considered by Tomescu and Medvedev (RECOMB 2016), who characterized these walks through the notion of omnitig . Omnitigs were shown to be relevant for the genome assembly problem from bioinformatics, where a genome sequence must be assembled from a set of reads from a sequencing experiment. Tomescu and Medvedev (RECOMB 2016) also proposed an algorithm for listing all maximal omnitigs, by launching an exhaustive visit from every edge. In this article, we prove new insights about the structure of omnitigs and solve several open questions about them. We combine these to achieve an O ( nm )-time algorithm for outputting all the maximal omnitigs of a graph (with n nodes and m edges). This is also optimal, as we show families of graphs whose total omnitig length is Ω( nm ). We implement this algorithm and show that it is 9--12 times faster in practice than the one of Tomescu and Medvedev (RECOMB 2016).

Genome assembly is the problem of reconstructing a genome sequence from a set of reads from a sequencing experiment. Typical formulations of the assembly problem admit in practice many genomic reconstructions, and actual genome assemblers usually output contigs, namely substrings that are promised to occur in the genome. To bridge the theory and practice, Tomescu and Medvedev [RECOMB 2016] reformulated contig assembly as finding all substrings common to all genomic reconstructions. They also gave a characterization of those walks (omnitigs) that are common to all closed edge-covering walks of a (directed) graph, a typical notion of genomic reconstruction. An algorithm for listing all maximal omnitigs was also proposed, by launching an exhaustive visit from every edge. In this paper, we prove new insights about the structure of omnitigs and solve several open questions about them. We combine these to achieve an O(nm)-time algorithm for outputting all the maximal omnitigs of a graph (with n nodes and m edges). This is also optimal, as we show families of graphs whose total omnitig length is Omega(nm). We implement this algorithm and show that it is 9-12 times faster in practice than the one of Tomescu and Medvedev [RECOMB 2016].