We devise an algorithm that approximately computes the number of paths of length k in a given directed graph with n vertices up to a multiplicative error of 1 ± ε. Our algorithm runs in time ε −2 4 k (n + m) poly(k). The algorithm is based on associating with each vertex an element in the exterior (or, Grassmann) algebra, called an extensor, and then performing computations in this algebra. This connection to exterior algebra generalizes a number of previous approaches for the longest path problem and is of independent conceptual interest. Using this approach, we also obtain a deterministic 2 k · poly(n) time algorithm to find a k-path in a given directed graph that is promised to have few of them. Our results and techniques generalize to the subgraph isomorphism problem when the subgraphs we are looking for have bounded pathwidth. Finally, we also obtain a randomized algorithm to detect k-multilinear terms in a multivariate polynomial given as a general algebraic circuit. To the best of our knowledge, this was previously only known for algebraic circuits not involving negative constants. arXiv:1804.09448v1 [cs.DS] 25 Apr 2018Longest Path. The Longest Path problem is the optimization problem to find a longest (simple) path in a given graph. Clearly, this problem generalizes the NP-hard Hamiltonian path problem [30]. We consider the decision version, the k-path problem, in which we wish to find a path of length k in a given graph G. It was proved fixed-parameter tractable avant la lettre [50], and a sequence of both iterative improvements and conceptual breakthroughs [11,4,7,40,16,27,63] have lead to the current state-of-the-art for undirected graphs: a randomized algorithm by Björklund et al. [9] in time 1.66 k · poly(n). For directed graphs, the fastest known randomized algorithm is by Koutis and Williams [43] in time 2 k · poly(n), whereas the fastest deterministic algorithm is due to Zehavi [66] in time 2.5961 k · poly(n).Subgraph isomorphism. The subgraph isomorphism problem generalizes the k-path problem and is one of the most fundamental graph problems [19,60]: Given two graphs H and G, decide whether G contains a subgraph isomorphic to H. This problem and its variants have a vast number of applications, covering areas such as statistical physics, probabilistic inference, and network analysis [49]. For example, such problems arise in the context of discovering network motifs, small patterns that occur more often in a network than would be expected if it was random. Thus, one is implicitly interested in the counting version of the subgraph isomorphism problem: to compute the number of subgraphs of G that are isomorphic to H. Through network motifs, the problem of counting subgraphs has found applications in the study of gene transcription networks, neural networks, and social networks [49]. Consequently, there is a large body of work dedicated to algorithmic discovery of network motifs [32,1,52,37,57,18,38,62,55]. For example, Kibriya and Ramon [39,53] use the ideas of Koutis and Williams [43] to enumerate...

Jaeger , Vertigan, and Welsh [15] proved a dichotomy for the complexity of evaluating the Tutte polynomial at fixed points: The evaluation is #Phard almost everywhere, and the remaining points admit polynomial-time algorithms. Dell, Husfeldt, and Wahlén [9] and Husfeldt and Taslaman [12], in combination with Curticapean [7], extended the #P-hardness results to tight lower bounds under the counting exponential time hypothesis #ETH, with the exception of the line y = 1, which was left open. We complete the dichotomy theorem for the Tutte polynomial under #ETH by proving that the number of all acyclic subgraphs of a given n-vertex graph cannot be determined in time exp o(n) unless #ETH fails.Another dichotomy theorem we strengthen is the one of Creignou and Hermann [6] for counting the number of satisfying assignments to a constraint satisfaction problem instance over the Boolean domain. We prove that all #P-hard cases are also hard under #ETH. The main ingredient is to prove that the number of independent sets in bipartite graphs with n vertices cannot be computed in time exp o(n) unless #ETH fails.In order to prove our results, we use the block interpolation idea by Curticapean [7] and transfer it to systems of linear equations that might not directly correspond to interpolation.

Given a zero-dimensional polynomial system consisting of n integer polynomials in n variables, we propose a certified and complete method to compute all complex solutions of the system as well as a corresponding separating linear form l with coefficients of small bit size. For computing l, we need to project the solutions into one dimension along O(n) distinct directions but no further algebraic manipulations. The solutions are then directly reconstructed from the considered projections. The first step is deterministic, whereas the second step uses randomization, thus being Las-Vegas.The theoretical analysis of our approach shows that the overall cost for the two problems considered above is dominated by the cost of carrying out the projections. We also give bounds on the bit complexity of our algorithms that are exclusively stated in terms of the number of variables, the total degree and the bitsize of the input polynomials.

We investigate the complexity of counting trees, forests and bases of matroids from a parameterized point of view. It turns out that the problems of computing the number of trees and forests with k edges are #W[1]-hard when parameterized by k. Together with the recent algorithm for deterministic matrix truncation by Lokshtanov et al. (ICALP 2015), the hardness result for k-forests implies #W[1]-hardness of the problem of counting bases of a matroid when parameterized by rank or nullity, even if the matroid is restricted to be representable over a field of characteristic 2. We complement this result by pointing out that the problem becomes fixed parameter tractable for matroids represented over a fixed finite field.

Solving (mixed) integer linear programs, (M)ILPs for short, is a fundamental optimization task. While hard in general, recent years have brought about vast progress for solving structurally restricted, (non-mixed) ILPs: n-fold, tree-fold, 2-stage stochastic and multi-stage stochastic programs admit efficient algorithms, and all of these special cases are subsumed by the class of ILPs of small treedepth.In this paper, we extend this line of work to the mixed case, by showing an algorithm solving MILP in time f (a, d) poly(n), where a is the largest coefficient of the constraint matrix, d is its treedepth, and n is the number of variables. This is enabled by proving bounds on the denominators of the vertices of bounded-treedepth (noninteger) linear programs. We do so by carefully analyzing the inverses of invertible submatrices of the constraint matrix. This allows us to afford scaling up the mixed program to the integer grid, and applying the known methods for integer programs. We trace the limiting boundary of our approach, showing that naturally related classes of linear programs have vertices of unbounded fractionality. Finally, we show that restricting the structure of only the integral variables in the constraint matrix does not yield tractable special cases.