Tsvi Kopelowitz

An Exponential Separation between Randomized and Deterministic Complexity in the LOCAL Model

Over the past 30 years numerous algorithms have been designed for symmetry breaking problems in the LOCAL model, such as maximal matching, MIS, vertex coloring, and edge coloring. For most problems the best randomized algorithm is at least exponentially faster than the best deterministic algorithm. We prove that these exponential gaps are necessary and establish numerous connections between the deterministic and randomized complexities in the LOCAL model. Each of our results has a very compelling take-away message: 1) Building on the recent randomized lower bounds of Brandt et al. [1], we prove that the randomized complexity of Δ-coloring a tree with maximum degree Δ is O(log Δ log n + log*n), for any Δ > = 55, whereas its deterministic complexity is Ω(log Δ n) for any Δ > = 3. This also establishes a large separation between the deterministic complexity of Δ-coloring and (Δ+1)-coloring trees. 2) We prove that any deterministic algorithm for a natural class of problems that runs in O(1) + o(log Δ n) rounds can be transformed to run in O(log*n - log*Δ + 1) rounds. If the transformed algorithm violates a lower bound (even allowing randomization), then one can conclude that the problem requires Ω(log Δ n) time deterministically. This gives an alternate proof that deterministically Δ-coloring a tree with small Δ takes Ω(log Δ n) rounds. 3) We prove that the randomized complexity of any natural problem on instances of size n is at least its deterministic complexity on instances of size √log n. This shows that a deterministic Ω(log Δ n) lower bound for any problem (Δ-coloring a tree, for example) implies a randomized Ω(log Δ log n) lower bound. It also illustrates that the graph shattering technique employed in recent randomized symmetry breaking algorithms is absolutely essential to the LOCAL model. For example, it is provably impossible to improve the 2O(√log log n) term in the complexities of the best MIS and (Δ+1)-coloring algorithms without also improving the 2O(√log n)-round Panconesi-Srinivasan algorithm.

Range non-overlapping indexing and successive list indexing

We present two natural variants of the indexing problem: In the range non-overlapping indexing problem, we preprocess a given text to answer queries in which we are given a pattern, and wish to find a maximal-length sequence of occurrences of the pattern in the text, such that the occurrences do not overlap with one another. While efficiently solving this problem, our algorithm even enables us to efficiently perform so in substrings of the text, denoted by given start and end locations. The methods we supply thus generalize the string statistics problem [4,5], in which we are asked to report merely the number of non-overlapping occurrences in the entire text, by reporting the occurrences themselves, even only for substrings of the text. In the related successive list indexing problem, during query-time we are given a pattern and a list of locations in the preprocessed text. We then wish to find a list of occurrences of the pattern, such that the ith occurrence is the leftmost occurrence of the pattern which starts to the right of the ith location given by the input list. Both problems are solved by using tools from computational geometry, specifically a variation of the range searching for minimum problem of Lenhof and Smid [12], here considered over a grid, in what appears to be the first utilization of range searching for minimum in an indexing-related context.

Suffix trays and suffix trists: structures for faster text indexing

Suffix trees and suffix arrays are two of the most widely used data structures for text indexing. Each uses linear space and can be constructed in linear time [3,5,6,7]. However, when it comes to answering queries, the prior does so in O(mlog|Σ|) time, where m is the query size, |Σ| is the alphabet size, and the latter does so in O(m+logn), where n is the text size. We propose a novel way of combining the two into, what we call, a suffix tray. The space and construction time remain linear and the query time improves to O(m+log|Σ|). We also consider the online version of indexing, where the indexing structure continues to update the text online and queries are answered in tandem. Here we suggest a suffix trist, a cross between a suffix tree and a suffix list. It supports queries in O(m+log|Σ|). The space and text update time of a suffix trist are the same as for the suffix tree or the suffix list.

On the longest common parameterized subsequence

The well-known problem of the longest common subsequence (LCS), of two strings of lengths n and m respectively, is O(nm)-time solvable and is a classical distance measure for strings. Another well-studied string comparison measure is that of parameterized matching, where two equal-length strings are a parameterized match if there exists a bijection on the alphabets such that one string matches the other under the bijection. All works associated with parameterized pattern matching present polynomial time algorithms. There have been several attempts to accommodate parameterized matching along with other distance measures, as these turn out to be natural problems, e.g., Hamming distance, and a bounded version of edit-distance. Several algorithms have been proposed for these problems. In this paper we consider the longest common parameterized subsequence problem which combines the LCS measure with parameterized matching. We prove that the problem is NP-hard, and then show a couple of approximation algorithms for the problem.

On-Line Indexing for General Alphabets via Predecessor Queries on Subsets of an Ordered List

The problem of Text Indexing is a fundamental algorithmic problem in which one wishes to preprocess a text in order to quickly locate pattern queries within the text. In the ever evolving world of dynamic and on-line data, there is also a need for developing solutions to index texts which arrive online, i.e. a character at a time, and still be able to quickly locate said patterns. In this paper, a new solution for on-line indexing is presented by providing an on-line suffix tree construction in O(log log n + log log |Σ|) worst-case expected time per character, where n is the size of the string, and Σ is the alphabet. This improves upon all previously known on-line suffix tree constructions for general alphabets, at the cost of having the run time in expectation. The main idea is to reduce the problem of constructing a suffix tree on-line to an interesting variant of the order maintenance problem, which may be of independent interest. In the famous order maintenance problem, one wishes to maintain a dynamic list L of size n under insertions, deletions, and order queries. In an order query, one is given two nodes from L and must determine which node precedes the other in L. In an extension to this problem, named the Predecessor search on Dynamic Subsets of an Ordered Dynamic List problem (POLP for short), it is also necessary to maintain dynamic subsets S1, · · · , Sk ⊆ L, such that given some u ∈ L it will be possible to quickly locate the predecessor of u in Si, for any integer 1 ≤ i ≤ k. This paper provides an efficient data structure capable of locating the predecessor of u in Si in O(log log n) worst-case time and answering order queries on L in O(1) worst-case time, while allowing updates to L in O(1) worst-case expected time and updates to the subsets in O(log log n) worst-case expected time. This improves over a previous data structure which may be implicitly obtained from Dietz [8], in which the updates to the sets and L are done in O(log log n) amortized expected time. In addition, the bounds shown here match the currently best known bounds for predecessor search in the RAM model. Furthermore, this paper improves or simplifies bounds for several additional applications, including fully-persistent arrays, the monotonic list labeling problem, and the Order-Maintenance Problem.

Towards real-time suffix tree construction

The quest for a real-time suffix tree construction algorithm is over three decades old. To date there is no convincing understandable solution to this problem. This paper makes a step in this direction by constructing a suffix tree online in time O(log n) per every single input symbol. Clearly, it is impossible to achieve better than O(log n) time per symbol in the comparison model, therefore no true real time algorithm can exist for infinite alphabets. Nevertheless, the best that can be hoped for is that the construction time for every symbol does not exceed O(log n) (as opposed to an amortized O(log n) time per symbol, achieved by current known algorithms). To our knowledge, our algorithm is the first that spends in the worst caseO(log n) per every single input symbol. We also provide a simple algorithm that constructs online an indexing structure (the BIS) in time O(log n) per input symbol, where n is the number of text symbols input thus far. This structure and fast LCP (Longest Common Prefix) queries on it, provide the backbone for the suffix tree construction. Together, our two data structures provide a searching algorithm for a pattern of length m whose time is

Orienting Fully Dynamic Graphs with Worst-Case Time Bounds

O(min(m {\rm log} |{\it \Sigma}|,m + {\rm log} n) + tocc)

Fully dynamic connectivity in O (log n (log log n ) 2 ) amortized expected time

, where tocc is the number of occurrences of the pattern.

Faster Worst Case Deterministic Dynamic Connectivity

In edge orientations, the goal is usually to orient (direct) the edges of an undirected network (modeled by a graph) such that all out-degrees are bounded. When the network is fully dynamic, i.e., admits edge insertions and deletions, we wish to maintain such an orientation while keeping a tab on the update time. Low out-degree orientations turned out to be a surprisingly useful tool for managing networks.

Selection in the Presence of Memory Faults, with Applications to In-place Resilient Sorting

Dynamic connectivity is one of the most fundamental problems in dynamic graph algorithms. We present a new randomized dynamic connectivity structure with