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Since its early days, combinatorial group theory was deeply interwoven with computability theory. In the last 20 years we have seen many new successful interactions between group theory and computer science. On one hand, groups played an important rule in many developments in complexity theory and automata theory. On the other hand, concepts from these computer science fields as well as efficient algorithms, cryptography, and data compression led to the formulation of new questions in group theory. The Dagstuhl Seminar Algorithmic Problems in Group Theory was aimed at bringing together researchers from group theory and computer science so that they can share their expertise. This report documents the material presented during the course of the seminar.

We prove that the compressed word problem and the compressed simultaneous conjugacy problem are solvable in polynomial time in hyperbolic groups. In such problems, group elements are input as words defined by straight line programs defined over a finite generating set for the group. We prove also that, for any infinite hyperbolic group $G$, the compressed knapsack problem in $G$ is ${\mathsf{NP}}$-complete., Improved version of the previous paper

We study the space complexity of sliding window streaming algorithms that check membership of the window content in a fixed context-free language. For regular languages, this complexity is either constant, logarithmic or linear [Moses Ganardi et al., 2016]. We prove that every context-free language whose sliding window space complexity is log_2(n) - omega(1) must be regular and has constant space complexity. Moreover, for every c in N, c >= 1 we construct a (nondeterministic) context-free language whose sliding window space complexity is O(n^(1/c)) \ o(n^(1/c)). Finally, we give an example of a deterministic one-counter language whose sliding window space complexity is Theta((log n)^2).

We study the average size of the minimal directed acyclic graph (DAG) with respect to so-called leaf-centric binary tree sources as studied by Zhang, Yang, and Kieffer. A leaf-centric binary tree source induces for every n >= 2 a probability distribution on all binary trees with n leaves. We generalize a result shown by Flajolet, Gourdon, Martinez and Devroye according to which the average size of the minimal DAG of a binary tree that is produced by the binary search tree model is Theta(n / log n).

Given finite-state automata (or context-free grammars) A, B over the same alphabet and a Parikh vector ~p, we study the complexity of deciding whether the number of words in the language of A with Parikh image ~p is greater than the number of such words in the language of B. Recently, this problem turned out to be tightly related to the cost problem for weighted Markov chains. We classify the complexity according to whether A and B are deterministic, the size of the alphabet, and the encoding of ~p (binary or unary).

Many XML documents are data-centric and do not make use of the inherent document order. Can we provide stronger compression for such documents through giving up order? We first consider compression via minimal dags (directed acyclic graphs) and study the worst case ratio of the size of the ordered dag divided by the size of the unordered dag, where the worst case is taken for all trees of size n. We prove that this worst case ratio is n / log n for the edge size and n log log n / log n for the node size. In experiments we compare several known compressors on the original document tree versus on a canonical version obtained by length-lexicographical sorting of subtrees. For some documents this difference is surprisingly large: reverse binary dags can be smaller by a factor of 3.7 and other compressors can be smaller by factors of up to 190.

Myasnikov et al. have introduced the knapsack problem for arbitrary finitely generated groups. In previous work, the authors proved that for each graph group, the knapsack problem can be solved in $\mathsf{NP}$. Here, we determine the exact complexity of the problem for every graph group. While the problem is $\mathsf{TC}^0$-complete for complete graphs, it is $\mathsf{LogCFL}$-complete for each (non-complete) transitive forest. For every remaining graph, the problem is $\mathsf{NP}$-complete., Comment: 26 pages, 2 figures

This report documents the program and the outcomes of Dagstuhl Seminar 16431 "Computation over Compressed Structured Data".

We study the space complexity of querying regular languages over data streams in the sliding window model. The algorithm has to answer at any point of time whether the content of the sliding window belongs to a fixed regular language. A trichotomy is shown: For every regular language the optimal space requirement is either in Theta(n), Theta(log(n)), or constant, where $n$ is the size of the sliding window.