Lower Bounds for 2-Dimensional Range Counting
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Lower Bounds for 2-Dimensional Range Counting Mihai Pătraşcu summer @
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Lower Bounds for 2-Dimensional Range Counting. Mihai P ătraşcu. summer @. Range Counting. SELECT count(*) FROM employees WHERE salary
Transcript of Lower Bounds for 2-Dimensional Range Counting
Lower Bounds for 2-Dimensional Range Counting
Mihai Pătraşcu
summer @
Range Counting
SELECT count(*)FROM employeesWHERE salary <= 70000 AND startdate <= 1998
’98 ’99’97 ’96
70000
69000
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71000
Some Theory
• d dimensions• range trees (roughly):
space S=nlgd-1n, query t=lgd-1n• space S=n2d, query t=O(1)
• geometric extensions: range = disks, half-spaces, polygons…
PROBLEM: lower bounds
The Semigroup Industry
Let (U,+) be a semigroup
Points have weights in U
Given w1,…,wn:
precompute S sums
query:
add t precomputed sums
w1
w2
w4
w5
w6
w7w3
w1+w2+w4+w5
Why “Industry”?
• tight semigroup lower bounds [Fredman JACM’81] [Chazelle FOCS’86]
• many, many excellent bounds for many range problems
Why are we so good at this?
[Caravaggio]
Semigroup = Low Dimension
• say Mem[17]=w2+w6
• can Mem[17] help with this query?
NO: cannot subtract w6
• Mem[17] described by bounding boxcan only be used when query dominates bounding box
“How well do rectangles decompose into rectangles?” X (disks, half-planes…) Y
w1 w2
w4
w5
w6
w7w3
All these objects are low-dimensional!
Computation = High Dimension
New concept…
weights come from a group (U,+,-)
• can cancel any weight => no bounding box• relevant information about Mem[17]:
O(1)-D rectangle → linear combination of n variables
• decomposability in O(1)-D → decomposability in n-D
• n-D means: geometry → information theory
The Ultimate Frontier
The cell-probe model:• plain old counting (weights 0,1)• each cell stores any function of inputs• query probes t cells (adaptively), computes any function
“Theory of the computer in your lap”
To boldly go where no one has gone before
General lack of group lower bounds
(never mind cell-probe!)[Chazelle STOC’95] Ω(lglg n)
[Fredman JACM’82]
[Chazelle FOCS’86]
[Agarwal ’9x, ’0x]
etc etc
yeah, we have nice semigroup lower boundsbut prove something in the group model
Our Small Step
If S=n lgO(1)n, then t=Ω(lg n / lglg n)
group model! …and cell-probe model! tight in 2D
Maybe not so giant leap for mankind… does not grow with d
=> really only relevant for 2D
Basic Idea
Remember:
Separating linear from polynomial space:
[P,Thorup STOC’06] Ω(lglg n)[P,Thorup SODA’06] --”-- randomized
[P,Thorup FOCS’06] Ω(lg n / lglg n)(“unnatural” problems; not tight)
NOW Ω(lg n / lglg n)(natural, fundamental problem; tight)
• space S=nlgd-1n, query t=lgd-1n• space S=n2d, query t=O(1)
“technique”
“framework”
“trick”
The trick:“Consider n / lg n queries, see what happens”
This paper: what happens for range counting
Hard Instance
The bit-reversal permutation (see FFT, etc):
e.g. π(6) = π(“110”) = “011” = 3
Well-known hard instance:• points at (x, π(x))• random query [0,a] X [0,b]
[in fact, many independent random queries]
x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
The Shuffle View
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The Shuffle View
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The Shuffle View
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The Shuffle View
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The Shuffle View
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The Shuffle View
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The Shuffle View
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
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The Shuffle View
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
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The Shuffle View
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
4
123
5678
77
33
55
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77
55
77
663
311
The Shuffle View
00
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33
44
55
66
77
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22 5
5
44
663
3
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66
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
4
123
5678
Shuffling the Queries
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
query [0,4] X [0,5]
4
123
5678
Shuffling the Queries
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
query [0,5] X [0,4]
77
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22 5
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5678
4
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Shuffling the Queries
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55
66
77
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22
x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
query [0,5] X [0,4]
55
44
77
553
3
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4
123
5678
4
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Shuffling the Queries
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22
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44
55
66
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x 0 1 2 3 4 5 6 7
π(x) 0 4 2 6 1 5 3 7
query [0,5] X [0,4]
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66
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Specifying a query=
Saying how it shuffles at every level
Proof Sketch (I)
• k queries, space S=> one probe revels lg = Θ(k lg(S/k))
bits of information about the queries• t probes => Θ(tk lg(S/k)) bits
• k=n / lg n S=n lgO(1)n t=o(lg
n / lglg n)=> o(k lg n) bitsrevealed by the probes about the queries
Sk
Proof Sketch (II)
• I(queries) ≤ I(shuffling at level 1) + I(shuffling at level 2) + …
+ I(shuffling at level lg n) ≤ o(k lg n)
• so for one level the data structure learns o(k) bits of information about the queries
[i.e. doesn’t know where most queries fit]
• so it cannot precompute useful sums[see Proceedings]
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