Time space trade offs for 2d range minimum queries
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Time-Space Trade-Offs for 2D Range Minimum Queries. Joint work with Gerth Stølting Brodal and S. Srinivasa Rao. Pooya Davoodi Aarhus University. Aarhus University, September 1, 2010. The 2D Range Minimum Problem. j ’. Input: an m x n - matrix of size N = m ∙ n , m ≤ n.

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Time-Space Trade-Offs for 2D Range Minimum Queries

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Time-SpaceTrade-Offs for 2D Range Minimum Queries

Joint work with Gerth Stølting Brodal and S. SrinivasaRao

Pooya Davoodi

Aarhus University

AarhusUniversity, September 1, 2010


The 2D Range Minimum Problem

j’

  • Input: an m x n-matrix of size N = m ∙ n, m ≤ n.

  • Preprocess the matrix s.t. range minimum queries are efficiently supported.

i’

Minimum


Models

j’

Encoding model

  • Queries can access data structure but not input matrix

Indexingmodel

  • Queriescanaccessdata structureand readinput matrix

i’

Minimum


Some Trivial Examples...

j’

i’

Minimum


Results


1D Range Minimum Queries

Fischer and Heun (2007)

NEW

NEW

Fischer (Latin 2010)


2D Range Minimum Queries

NEW

NEW

NEW Proof

Demain et al. (2009)


1D


LowerBound (1D, Encoding)

  • For each input array consider the Cartesiantree

  • Eachbinarytree is a possibleCartesiantree

  • RMQ queriescanreconstruct the Cartesiantree

  • # Cartesiantrees is

  • # bits ≥ = 2n - Θ(log n)


Upper Bound (1D, Encoding)

  • For an input array consider the Cartesiantree

  • Succintrepresentationusing4n+o(n) bits and O(1) query time (Sadakane 2007)

  • Improved to 2n+o(n) (Fischer 2010)


Upper Bounds (1D, Indexing)

C

  • Build encoding O(n/c) bit structure for block minimums

  • RMQ = query to encodingstructure + 3c elements, i.e. query time O(c)

block minimums (implicit)


LowerBounds (1D, Indexing)

ThmSpace N/cbits impliesΩ(c)query time

  • Consider N/cqueries for cN/c different {0,1} inputs with exactly one zero in each block

  • cN/c / 2N/c inputs sharesome data structure

  • Everyquery is a

    decision tree of

    height≤d

N/c

q1

q2

qN/c


LowerBounds (1D, Indexing)

cont.

  • Combinequeries to decision treeidentifying input

  • Prunenon-reachable branches

    # zeroesonanypath ≤ N/c

  • query time d = Ω(c)

N/c

q1

q2

qN/c

  • N


2D


LowerBounds (2D, Indexing)

C

  • As for 1D consider {0,1} matrices and partition the array intoblocks of c elements eachcontainingexactlyonezero

  • As for 1D an algorithmbeingable to identify the zero in eachblockusingN/c bits willrequiretime Ω(c)


Upper Bounds (2D, Indexing)

  • O(1) time using O(N)words Atallahand Yuan (SODA 2010)

  • Usingtwo-levels of

    recursion, tabulating

    micro-blocks of size

    loglogmxloglogn

    O(1) time usingO(N) bits


Upper Bounds (2D, Indexing)

cont.

  • Thm O(N/c ∙ log c) bits and O(c logc)query time

  • Buildlog cindexingstructures for compressed matrices for blocksizes2i x c/2i, eachusingO(N/c) bits and canlocateO(1)blockswith minimum key in O(1) time

  • Query: O(1) blocks for eachblocksize in time O(c) + elements not covered by blocks in time O(c log c)


Upper Bounds (2D, Encoding)

  • Translate input matrix into rank matrix using O(N log n) bits

  • Applyindexstructure to rank matrix usingO(N) bits achievingO(1)query time

input matrix

rank matrix


NEW Proof

LowerBound (2D, Encoding)Demaine et al. 2009

  • Define a set of

    matrices where the RMQ answersdifferamong all matrices

  • Bits required is at least

    log = Ω(N log m)


Conclusion


1D Range Minimum Queries

Fischer and Heun (2007)

NEW

(matching upper bound)

Fischer (Latin 2010)


2D Range Minimum Queries

NEW

?

NEW

?

NEW Proof

Demain et al. (2009)


ThankYou


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