Cache-Oblivious Selection in Sorted X+Y Matrices

Let X[0..n-1] and Y[0..m-1] be two sorted arrays, and define the mxn matrix A by A[j][i]=X[i]+Y[j]. Frederickson and Johnson gave an efficient algorithm for selecting the k-th smallest element from A. We show how to make this algorithm IO-efficient. Our cache-oblivious algorithm performs O((m+n)/B) IOs, where B is the block size of memory transfers

Online Sorting via Searching and Selection

In this paper, we present a framework based on a simple data structure and parameterized algorithms for the problems of finding items in an unsorted list of linearly ordered items based on their rank (selection) or value (search). As a side-effect of answering these online selection and search queries, we progressively sort the list. Our algorithms are based on Hoare's Quickselect, and are parameterized based on the pivot selection method. For example, if we choose the pivot as the last item in a subinterval, our framework yields algorithms that will answer q<=n unique selection and/or search queries in a total of O(n log q) average time. After q=\Omega(n) queries the list is sorted. Each repeated selection query takes constant time, and each repeated search query takes O(log n) time. The two query types can be interleaved freely. By plugging different pivot selection methods into our framework, these results can, for example, become randomized expected time or deterministic worst-case time. Our methods are easy to implement, and we show they perform well in practice

Cache-Oblivious Iterated Predecessor Queries via Range Coalescing

In this paper we develop an optimal cache-oblivious data structure that solves the iterated predecessor problem. Given k static sorted lists L[subscript 1],L[subscript 2],…,L[subscript k] of average length n and a query value q, the iterated predecessor problem is to find the largest element in each list which is less than q. Our solution to this problem, called “range coalescing”, requires O(log[subscript B+1]n+k/B) memory transfers for a query on a cache of block size B, which is information-theoretically optimal. The range-coalescing data structure consumes O(kn) space, and preprocessing requires only O(kn / B) memory transfers with high probability, given a tall cache of size M=Ω(B[superscript 2])