Timothy M. Chan's Publications: Sorting, selection, and other one-dimensional problems


Selection and sorting in the "restore" model

(with
J. Ian Munro and Venkatesh Raman)

We consider the classical selection and sorting problems in a model where the initial permutation of the input has to be restored after completing the computation. While the requirement of the restoration is stringent compared to the classical versions of the problems, this model is more relaxed than a read-only memory where the input elements are not allowed to be moved within the input array.

We first show that for a sequence of n integers, selection (finding the median or more generally the k-th smallest element for a given k) can be done in O(n) time using O(lg n) words of extra space in this model. In contrast, no linear-time selection algorithm is known which uses polylogarithmic space in the read-only memory model.

For sorting n integers in this model, we first present an O(n lg n)-time algorithm using O(lg n) words of extra space. When the universe size U is polynomial in n, we give a faster O(n)-time algorithm (analogous to radix sort) which uses O(n^eps) words of extra space for an arbitrarily small constant eps>0. More generally, we show how to match the time bound of any word-RAM integer-sorting algorithm using O(n^eps) words of extra space. In sharp contrast, there is an Omega(n^2/S)-time lower bound for integer sorting using O(S) bits of space in the read-only memory model. Extension of our results to arbitrary input types beyond integers is not possible: for "indivisible" input elements, we can prove the same Omega(n^2/S) lower bound for sorting in our model.

En route, we develop linear-time in-place algorithms to extract leading bits of the input array and to compress and decompress strings with low entropy; these techniques may be of independent interest.


Finding median in read-only memory on integer input

(with
J. Ian Munro and Venkatesh Raman)

Starting with Munro and Paterson (1980), the selection or median-finding problem has been extensively studied in the read-only memory model and in streaming models. Munro and Paterson's deterministic algorithm and its subsequent refinements require at least polylogarithmic or logarithmic space, whereas the algorithms by Munro and Raman (1996) and Raman and Ramnath (1999) can be made to use just O(1) storage cells but take O(n^{1+eps}) time for an arbitrarily small constant eps>0.

In this paper, we show that faster selection algorithms in read-only memory are possible if the input is a sequence of integers. For example, one algorithm uses O(1) storage cells and takes O(n lg U) time where U is the universe size. Another algorithm uses O(1) storage cells and takes O(n lg n lglg U) time. We also describe an O(n)-time algorithm for finding an approximate median using O(lg^eps U) storage cells.

All our algorithms are simple and deterministic. Interestingly, one of our algorithms works by making multiple calls to the textbook algorithm to find the majority of a sequence of bits. This is to find the `centroid' of the trie of the binary representation of the sequence of integers. This technique could be of independent interest.


Linear-space data structures for range minority query in arrays

(with
Stephane Durocher, Matthew Skala, and Bryan T. Wilkinson)

We consider range queries in arrays that search for low-frequency elements: least frequent elements and alpha-minorities. An alpha-minority of a query range has multiplicity no greater than an alpha fraction of the elements in the range. Our data structure for the least frequent element range query problem requires O(n) space, O(n^{3/2}) preprocessing time, and O(sqrt{n}) query time. A reduction from boolean matrix multiplication to this problem shows the hardness of simultaneous improvements in both preprocessing time and query time. Our data structure for the alpha-minority range query problem requires O(n) space and O(1/alpha) query time, and allows alpha to be specified at query time.


Linear-space data structures for range mode query in arrays

(with
Stephane Durocher, Kasper Green Larsen, Jason Morrison, and Bryan T. Wilkinson)

A mode of a multiset S is an element a in S of maximum multiplicity; that is, a occurs at least as frequently as any other element in S. Given an array A[1:n] of n elements, we consider a basic problem: constructing a static data structure that efficiently answers range mode queries on A. Each query consists of an input pair of indices (i, j) for which a mode of A[i:j] must be returned. The best previous data structure with linear space, by Krizanc, Morin, and Smid (ISAAC 2003), requires O(sqrt(n) loglog n) query time. We improve their result and present an O(n)-space data structure that supports range mode queries in O(sqrt(n / log n)) worst-case time. Furthermore, we present strong evidence that a query time significantly below sqrt(n) cannot be achieved by purely combinatorial techniques; we show that boolean matrix multiplication of two sqrt(n) by sqrt(n) matrices reduces to n range mode queries in an array of size O(n). Additionally, we give linear-space data structures for orthogonal range mode in higher dimensions (queries in near O(n^(1-1/2d)) time) and for halfspace range mode in higher dimensions (queries in O(n^(1-1/d^2)) time).


Persistent predecessor search and orthogonal point location on the word RAM

We answer a basic data structuring question (for example, raised by Dietz and Raman back in SODA 1991): can van Emde Boas trees be made persistent, without changing their asymptotic query/update time? We present a (partially) persistent data structure that supports predecessor search in a set of integers in {1,...,U} under an arbitrary sequence of n insertions and deletions, with O(loglog U) expected query time and expected amortized update time, and O(n) space. The query bound is optimal in U for linear-space structures and improves previous near-O((loglog U)^2) methods.

The same method solves a fundamental problem from computational geometry: point location in orthogonal planar subdivisions (where edges are vertical or horizontal). We obtain the first static data structure achieving O(loglog U) worst-case query time and linear space. This result is again optimal in U for linear-space structures and improves the previous O((loglog U)^2) method by de Berg, Snoeyink, and van Kreveld (1992). The same result also holds for higher-dimensional subdivisions that are orthogonal binary space partitions, and for certain nonorthogonal planar subdivisions such as triangulations without small angles. Many geometric applications follow, including improved query times for orthogonal range reporting for dimensions >= 3 on the RAM.

Our key technique is an interesting new van-Emde-Boas-style recursion that alternates between two strategies, both quite simple.


Counting inversions, offline orthogonal range counting, and related problems

(with
Mihai Patrascu)

We give an O(n sqrt{lg n})-time algorithm for counting the number of inversions in a permutation on n elements. This improves a long-standing previous bound of O(n lg n/lg lg n) that followed from Dietz's data structure [WADS'89], and answers a question of Andersson and Petersson [SODA'95]. As Dietz's result is known to be optimal for the related dynamic rank problem, our result demonstrates a significant improvement in the offline setting. Our new technique is quite simple: we perform a "vertical partitioning" of a trie (akin to van Emde Boas trees), and use ideas from external memory. However, the technique finds numerous applications: for example, we obtain

As a bonus, we also give a simple (1+epsilon)-approximation algorithm for counting inversions that runs in linear time, improving the previous O(n lg lg n) bound by Andersson and Petersson.


Quake heaps: a simple alternative to Fibonacci heaps

This note describes a data structure that has the same theoretical performance as Fibonacci heaps, supporting decrease-key operations in O(1) amortized time and delete-min operations in O(log n) amortized time. The data structure is simple to explain and analyze, and may be of pedagogical value.


Comparison-based time-space lower bounds for selection

We establish the first nontrivial lower bounds on time-space tradeoffs for the selection problem. We prove that any comparison-based randomized algorithm for finding the median requires Omega(n log log_S n) expected time in the RAM model (or more generally in the comparison branching program model), if we have S bits of extra space besides the read-only input array. This bound is tight for all S >> log n, and remains true even if the array is given in a random order. Our result thus answers a 16-year-old question of Munro and Raman, and also complements recent lower bounds that are restricted to sequential access, as in the multi-pass streaming model [Chakrabarti et al., SODA 2008].

We also prove that any comparison-based, deterministic, multi-pass streaming algorithm for finding the median requires Omega(n log^* (n/s) + n log_s n) worst-case time (in scanning plus comparisons), if we have s cells of space. This bound is also tight for all s >> log^2 n. We get deterministic lower bounds for I/O-efficient algorithms as well.

All proofs in this paper involve "elementary" techniques only.


Necklaces, convolutions, and X+Y

(with
David Bremner, Erik D. Demaine, Jeff Erickson, Ferran Hurtado, John Iacono, Stefan Langerman, Mihai Patrascu, and Perouz Taslakian)

We give subquadratic algorithms that, given two necklaces each with n beads at arbitrary positions, compute the optimal rotation of the necklaces to best align the beads. Here alignment is measured according to the l_p norm of the vector of distances between pairs of beads from opposite necklaces in the best perfect matching. We show surprisingly different results for p=1, p=2, and p=infty. For p=2, we reduce the problem to standard convolution, while for p=infty and p=1, we reduce the problem to (min,+) convolution and (median,+) convolution. Then we solve the latter two convolution problems in subquadratic time, which are interesting results in their own right. These results shed some light on the classic sorting X+Y problem, because the convolutions can be viewed as computing order statistics on the antidiagonals of the X+Y matrix. All of our algorithms run in o(n^2) time, whereas the obvious algorithms for these problems run in Theta(n^2) time.


Fun-sort---or the chaos of unordered binary search

(with
Therese Biedl, Erik D. Demaine, Rudolf Fleischer, Mordecai Golin, James A. King, and J. Ian Munro)

Usually, binary search only makes sense in sorted arrays. We show that insertion sort based on repeated "binary searches" in an initially unsorted array also sorts n elements in time Theta(n^2 log n). If n is a power of two then the expected termination point of a binary search in a random permutation of n elements is exactly the cell where the element should be if the array was sorted. We further show that we can sort in expected time Theta(n^2 log n) by always picking two random cells and swapping their contents if they are not ordered correctly.


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Timothy Chan (Last updated April 2018)