We introduce the contiguous art gallery problem which is to guard the boundary of a simple polygon with a minimum number of guards such that each guard covers exactly one contiguous portion of the boundary. Art gallery problems are often NP-hard. In particular, it is NP-hard to minimize the number of guards to see the boundary of a simple polygon, without the contiguity constraint. This paper is a merge of three concurrent works [Ahmad Biniaz et al., 2024; Magnus Christian Ring Merrild et al., 2024; Eliot W. Robson et al., 2024] each showing that (surprisingly) the contiguous art gallery problem is solvable in polynomial time. The common idea of all three approaches is developing a greedy function that maps a point on the boundary to the furthest point on the boundary so that the contiguous interval along the boundary between them could be guarded by one guard. Repeatedly applying this function immediately leads to an OPT+1 approximation. By studying this greedy algorithm, we present three different approaches that achieve an optimal solution. The first and second approach apply this greedy algorithm from different points on the boundary that could be found in advance or on the fly while traversing along the boundary (respectively). The third approach represents this function as a piecewise linear rational function, which can be reduced to an abstract arc cover problem involving infinite families of arcs. We identify other problems that can be represented by similar functions, and solve them via the third approach. From the combinatorial point of view, we show that any n-vertex polygon can be guarded by at most ⌊(n-2)/2⌋ guards. This bound is tight because there are polygons that require this many guards.

We present an optimal partially-persistent external-memory search tree with amortized I/O bounds matching those achieved by the non-persistent Bε-tree by Brodal and Fagerberg [SODA 2003]. In a partially-persistent data structure, each update creates a new version. All past versions can be queried, but only the current version can be updated. Operations should be efficient with respect to the size Nv of the accessed version v. For any parameter 0 < ε < 1, our data structure supports insertions and deletions in amortized (Equation Presented) I/Os, where B is the external-memory block size. It also supports successor and range reporting queries in amortized (Equation Presented) I/Os, where K is the number of keys reported. The space usage of the data structure is linear in the total number of updates. We make the standard and minimal assumption that the internal memory has size M ≥ 2B. The previous state-of-the-art external-memory partially-persistent search tree by Arge, Danner and Teh [JEA 2003] supports all operations in worst-case (Equation Presented) I/Os, matching the bounds achieved by the classical B-tree by Bayer and McCreight [Acta Informatica 1972]. Our data structure successfully combines buffering updates with partial persistence. The I/O bounds can also be achieved in the worst-case sense, by slightly modifying our data structure and under the requirement that the memory size M = Ω(B1−ε log2(maxv Nv)). For updates, where the I/O bound is o(1), we assume that the I/Os are performed evenly spread out among the updates (by performing buffer-overflows incrementally). The worst-case result slightly improves the memory requirement over the previous ephemeral external-memory dictionary by Das, Iacono, and Nekrich (ISAAC 2022), who achieved matching worst-case I/O bounds but required M = Ω(B logB N), where N is the size of the current dictionary.

We present a thorough investigation of the All Nearest Smaller Values (ANSV) problem from a practical perspective. The ANSV problem is defined as follows: given an array A consisting of n values, for each entry A_i compute the largest index l < i and the smallest index r > i such that A_i > A_l and A_i > A_r, i.e., the indices of the nearest smaller values to the left and to the right of A_i. The ANSV problem was solved by Berkman, Schieber, and Vishkin [J. Algorithms, 1993] in the PRAM model. Their solution in the CREW PRAM model, which we will refer to as the BSV algorithm, achieves optimal O (n) work and O(log n) span. Until now, the BSV algorithm has been perceived as too complicated for practical use, and we are not aware of any publicly available implementations. Instead, the best existing practical solution to the ANSV problem is the implementation by Shun and Zhao presented at DCC'13. They implemented a simpler O(n log n)-work algorithm with an additional heuristic first proposed by Blelloch and Shun at ALENEX'11. We refer to this implementation as the BSZ algorithm. In this paper, we implement the original BSV algorithm and demonstrate its practical efficiency. Despite its perceived complexity, our results show that its performance is comparable to the BSZ algorithm. We also present the first theoretical analysis of the heuristic implemented in the BSZ algorithm and show that it provides a tunable trade-off between optimal work and optimal span. In particular, we show that it achieves \mathcalO \!łeft(nłeft(1 + \fracłogn k \right)\right) work and \mathcalO \!łeft(k(1+łog\fracn k )\right) span, for any integer parameter 1 łe k łe n. Thus, for k = Θ\!łeft(łog n\right), the BSZ algorithm can be made to be work-optimal, albeit at the expense of increased span compared to BSV. Our discussion includes a detailed examination of different input types, particularly highlighting that for random inputs, the low expected distance between values and their nearest smaller values renders simple algorithms efficient. Finally, we analyze the input/output (I/Oxspace) complexities of the BSV algorithm.

We present the first fully-persistent external-memory search tree achieving amortized I/O bounds matching those of the classic (ephemeral) B-tree by Bayer and McCreight. The insertion and deletion of a value in any version requires amortized O(logB Nv) I/Os and a range reporting query in any version requires worst-case O(logB Nv + K/B) I/Os, where K is the number of values reported, Nv is the number of values in the version v of the tree queried or updated, and B is the external-memory block size. The data structure requires space linear in the total number of updates. Compared to the previous best bounds for fully persistent B-trees [Brodal, Sioutas, Tsakalidis, and Tsichlas, SODA 2012], this paper eliminates from the update bound an additive term of O(log2 B) I/Os. This result matches the previous best bounds for the restricted case of partial persistent B-trees [Arge, Danner and Teh, JEA 2003]. Central to our approach is to consider the problem as a dynamic set of two-dimensional rectangles that can be merged and split.