Loading in 5 sec....

Time-Space Trade-Offs for 2D Range Minimum QueriesPowerPoint Presentation

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

- 57 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Time-Space Trade-Offs for 2D Range Minimum Queries' - solada

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### 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

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

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)

ThankYou

Download Presentation

Connecting to Server..