Kinetic Algorithms: Approximation and Trade-offs Pankaj K. Agarwal Duke University.
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Transcript of Kinetic Algorithms: Approximation and Trade-offs Pankaj K. Agarwal Duke University.
![Page 1: Kinetic Algorithms: Approximation and Trade-offs Pankaj K. Agarwal Duke University.](https://reader036.fdocuments.us/reader036/viewer/2022062408/56649e955503460f94b99bd2/html5/thumbnails/1.jpg)
Kinetic Algorithms:
Approximation and Trade-offs
Pankaj K. AgarwalDuke University
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Motivation
Applications• Location based services
• Animation
• Physical simulation
• Mobile and wireless networks
Need algorithms and data structures for processing, analyzing, querying moving objects
Dynamic data structures not suitable for handling moving objects
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Modeling Motion
p(t) = (x(t),y(t)): Position of p at time tx(t), y(t): polynomialsDegree of motion: max degree of x(), y()Linear motion: Degree = 1
• p(t) = a t + b, a, b in R2
Mostly assume motion to be linearTrajectory of points can changeTrajectory can be piecewise linear
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Early WorkOff-line setting: Entire motion known in advanceBound the # combinatorial/topological changes in
geometric attributes under algebraic motion [Atallah 1985]
• Convex hull, closest pair, Voronoi diagram
# combinatorial changes in
•Convex hull: ≈n2
•closest pair: (n2)
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Early Work: Open Problems
# edge-flips in Delaunay triangulation of a point set, each point moving with fixed velocity• Upper Bound O(n3)• Lower Bound (n2)
# changes in the smallest disk containing points• Smallest disk is defined by 2 or 3 points lying on its
boundary
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Kinetic Data Structures[Basch, Guibas, Hershberger 1999]
Event based frameworkStore some auxiliary information to expedite the
simulation
•A<B, C<D, B<D hold: no computation necessary (certificates)
•A=B, C=D, or B=D: update structure (events)
external event
internal event
A B C D
DB
D
A B DC
A
B DCAB DC
A
A
B DC AB DCD
A
B CB DC A
A
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Kinetic Data Structure (KDS)
Maintain a set of certficatesCertificates provide a proof of the correctness of
the structureDetermine when a certificate fails: event
• Event times are roots of certain polynomials
Update the structure at an event and compute new certficates
Store events in a global priority queue
Proof of Correctnes
s
Certificate Failure
Proof Update
Structure Update
first event in global queue
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Kinetic Data Structures (KDS)Performance of KDS measured as# events (efficiency)# certificates (compactness)Time spent at each event (locality)Efficient KDS developed for many problems[A. et al. 2001][Guibas 2004]
IssuesToo many events for many KDSComputing event times is expensiveQuerying moving objects
• No need to maintain the structure at all times
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Trade Offs in KDS
Efficiency vs Approximation
Efficiency vs Accuracy
Querying Moving Objects• Range searching, nearest-neighbor searching on
moving points
• No need to maintain the structure at all times
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I. Efficiency vs Approximation
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KDS using Coresets
S: Set of n moving points in R2
Maintain the diameter (width, smallest enclosing box) of S
[A., Guibas, Hershberger, Veach]
• Diametral pair can change (n2) times• KDS with ~ n2 events
Can we maintain the approximate diameter of S more efficiently?• Is there a small subset Q of S s.t. for all t diam(Q(t)) ≥ (1-) diam(S(t))
Q: coreset of S
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Extent of FunctionsF={f1, …, fn}: d-variate functions
• UF: Upper envelope of F UF(x) = maxi fi(x)
• LF: Lower envelope of F LF(x) = mini fi(x)
Extent: EF(x) = UF(x) - LF(x) -kernel: G is -kernel of F if (1-) EF(x) ≤ EG(x)
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Coresets for Moving Points
S: Set of n moving points in R2 (u,S(t)): Directional width of S(t) in direction u A subset Q is -kernel of S if For u in S1, t in R
(1-)(u,S(t)) ≤(u,Q(t))
fi(u,t): ‹pi(t), u›, F={f1…fn}
Claim: (u,S(t)) = EF(t) -kernel of F -kernel of S
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Kernels of Moving Points
Theorem [A., Har-Peled, Varadarajan]
F={f1, …, fn}: d-variate polynomials of fixed degree;
> 0 parameter
An -kernel of F of size 1/O(1) can be computed in time O(n+ 1/O(1)).
Corollary: S: n points moving with fixed velocity in 2D, > 0 parameter.
An -kernel of S of size O(1/3/2) can be computed in time O(n+ 1/3).
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Maintaining a Bounding Box
Maintain an -approximation of the bounding box of SCompute an -kernel Q of S Smallest Bounding box defined by:
left-,right, top, and bottom-most pointsUse KDS to maintain these 4 points of QEvents: When one of them changes
Same approach works for maintaining
width, diameter, … approximately
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Bounding Box: Quality of Kernels10,000 moving pointsTrajectories linear of quadraticError < 0.02 for kernel of size 32
Linear Motion Quadratic Motion
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Bounding Box: # Events
Exact Algorithm Approximation Algorithm
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Kinetic Convex Hull with Coresets
Quality of Approximation
Quality over 200 Random Directions
Quality of WidthQuality of Diameter
Convex hull of 10,000 moving points
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Kinetic Event Distribution
Original Set Coreset
* Input: 10,000 linearly moving points
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Delaunay triangulation in R2
• O(n3) edge flips
An arbitrary triangulation in R2
• ≈n2 edge flips [A., Wang, Yu 2004]
Can we maintain an almost Delaunay triangulation with ≈n2 edge flips?
Kinetic Triangulations
[A., Guibas, Gao, Koltun, Sharir 2006]Maintain a subgraph of Delaunay triangulation that • contains (n) Delauanay edges •contains all wide Delaunay edges • performs ≈ n2 edge flips
Is there a good definition of almost Delaunay triangulation?
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Efficiency vs Accuracy
Robust KDS
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Event Scheduling in KDS
The kinetic data structure framework
Events: Computing roots of a polynomial
KDS assumes events are processed in correct order* Need exact root comparison; EXPENSIVE!
* Need degeneracy handling (simultaneous events); PAINFUL!
Proof of Correctness
Certificate Failure
Proof Update
Structure Update
first event in global queue
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Out-of-Order Event Processing
What if using floating point arithmetic to compute and compare event times inexactly? * Pros: cheaper arithmetic operations* Cons: events may now be processed in the wrong order
)(tA
)(tB
)(tC
In-order: ABCACBCABCBAOut-of-order: ABCBACBCA
Not scheduled because its computed event time is before current time
scheduled processed
t
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Out-of-Order Event Processing
Issues in out-of-order event processing
* Does the KDS fall into an infinite loop?
* Can an event be delayed for too long?
* Can error in the maintained structure be too large?
[Abam, A., de Berg, Yu, 2006]
Robust KDS to address these issues
• KDS is correct at all times except near the event times
• No event is delayed too long
• Bonus: Degeneracies are handled automatically
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Model of Robust KDS
Root computation procedure CROP
* : input polynomial; : error bound in CROP
* CROP( ) does the following
(1) find set of disjoint, open event intervals
s.t. each and they cover all roots
(2) find parity of the number of roots lying in each
(3) return intervals with odd number of roots
)(tf )(tf
kII ,,1 iI
iI
1I 2I3I
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Computing Event Times
1I 2I3I
+_ + _ +_ + _
currt
last1last
)sign( currt If Certificate conforms to schedule a future event at ;
Otherwise schedule a past event at .
last 1last
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Computing Event Times
: failing certificate; : polynomial associated withc c c
A past event…
A future event…
currtlast 1lastcurrtlast 1last
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Robust Kinetic Sorting
I may encounter a past event…
The new EventTime(.)
* Almost the same as traditional kinetic sorting algorithm…
(but not always, e.g., robust kinetic convex hull)
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Nice Properties
The KDS does not fall into an infinite loop
List is correct except within -neighborhood of actual event times
t
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Nice Properties
Events may be delayed by at most time long
Even when list is incorrect, it is still close to true sorted list geometrically
)( nO
maxcurrcurr )(ˆ)( Vntxtx ii ix : i-th pt in maintained list
ix̂ : i-th pt in sorted list
maxV : maximum velocity over time interval currcurr , tt
currt
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Experimental Results: Kinetic Sorting
Input
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Experimental Results
Input
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Experimental Results
Input
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Other Robust KDS
[Abam, A., de Berg, Yu, 2006]
Kinetic tournamentConvex hull, kd-tree, range-tree, …
Is there a robust KDS for Delaunay triangulation?
• Find a sequence of edge-flips to convert a self-intersecting triangulation to Delaunay triangulation
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Soft KDS
[Czumaj, Sohler 2005]
Approximate KDSRepair the structure only when necessaryUse the ideas from property testing to ensure
KDS is almost correct with high probabilityCompetitive analysis to measure the performance
of KDS
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III. Querying Moving Objects
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Kinetic Range Searching
S: Set of points, each moving with fixed velocity in R2
Preprocess S into a data structure: (Q1) Given rectangle R at time t, report all points S(t)∩R (Q2) Given R and time interval [a,b], report all points of
that pass thru R during the time interval [a,b]
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KDS ApproachKientic range trees [A., Arge, Erickson 2003]
• O(n log n) space, O(log n + k) query (Q1)• Use KDS approach to update range tree (n2) events; O(log2 n) (amortized) time at each event• Queries have to arrive in chronological order
Kinetic kd-trees [A. Gao, Guibas 2003]• O(n) space, O(n1/2 + k) query (Q1)(n2) events; O(log2 n) (amortized) time at each event• Queries have to arrive in chronological order
What if queries do not arrive in chronological order? Why spend time processing events?
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Kinetic Range SearchingPartition tree based approach [A., Arge, Erickson]O(n) space, O(n1/2 + k) query timeO(log2 n) insertion/deletion of a point
Answering (Q1) query
A similar approach works for (Q2) queries
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Time-Responsive Indexing
Time-responsiveness* Near future queries need to be answered more quickly
* Optimize structure for near future
* Approximate distant future
Results [A., Arge, Erickson, Yu, 2004]
* Orthogonal range queries in R1 , R2
~n space, ((tq)/n)1/2 + logO(1) n + k query time
(tq): # events between current time and tq
t
x4l
3l
2l
1l
)()( nOtq
)()( 2ntq
kn polylog
kn 2/1
(near future)(distant future)
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: set of linearly moving points in R1
* Given interval and time , report
In tx-plane, reduces to stabbing query* Report all lines intersecting a vertical
segment
Overall structure* Divide tx-plane into slabs
* i-th slab contains events (vertices)
* A window structure for each slab to answer stabbing query
],[}{ 21 xxtq
],[ 21 xx qt ],[)( 21 xxtS q )(tS
Example: 1D Time-Responsive Indexing
nlogni 2
x
tqt
2x
1x
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Window Structure
Hierarchical triangulation of the i-th slab* triangles
* Each triangle intersects at most lines
Partition tree for each triangle* Size: , query time:
Overall* Space: , query:
(note that )
* Update every other events
( amortized per event)
n~ kni polylog2 2/
in 2/~i2
i2 2/2i
cutting tree
partition tree
)/2( ni
x
t
-cutting
iqt 2)(
)(nO)( nO
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References
[Abam, Agarwal, de Berg, Yu, 2006] Out-of-order event processing in kinetic data structures. ESA’06.
[Abam, de Berg, 2005] Kinetic sorting and kinetic convex hulls. SoCG’05.
[Agarwal, Arge, Erickson, 2003] Indexing moving points. J. Comput. Syst. Sci., 66(1).
[Agarwal, Arge, Erickson, Yu, 2004] Efficient tradeoff schemes in data structures for querying moving objects. ESA’04.
[Agarwal, Arge, Vahrenhold, 2001] Time responsive external data structures for moving points. WADS’01.
[Agarwal, Gao, Guibas, Koltun, Sharir, 2006] Stable Delaunay triabgulation, manuscript.
[Agarwal, Har-Peled, Varadarajan, 2004] Approximating extent measures of points. J. ACM, 51(4).
[Agarwal, Wang, Yu] Kinetic triangulation, SOCG’04.
[Czumaz, Sohler, 2005] Soft kinetic data structures, SODA.
[Guibas 2004] Kinetic data structures, Handbook of DCG, 2nd edition,
[Yu, Agarwal, Poreddy, Varadarajan, 2004] Practical methods for shape fitting and kinetic data structures using coresets. SoCG’04.
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Example: Kinetic Sorting
)(tA
)(tB
)(tC
CBAList: ABCBACBCA
scheduled processed
currtcurrt currt
currt
Scheduled as a past event because current configuration is inconsistent with )()(sign currcurr tCtB