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The First-Order Variable Hierachy on Ordered Graphs
Benjamin Rossman
MIT
Bounded Variable Logics
The variable complexity of a first-order formula is the maximum number of free variables in a subformula of .
FOm = { first-order formulas with variable complexity m }
Example: (in the language {E,<} of ordered graphs)
9x1 9x2 ( x1<x2 Æ Ex1x2 Æ 9x3 ( x2<x3 Æ Ex2x3 Æ
9x4 ( x3<x4 Æ Ex3x4 Æ 9x5 ( x4<x5 Æ Ex4x5 ).
This sentence expresses "there is an increasing path of length 5". Its variable complexity is 2.
Bounded Variable Logics
The variable complexity of a first-order formula is the maximum number of free variables in a subformula of .
FOm = { first-order formulas with variable complexity
Example:9x 9y ( x<y Æ Exy Æ 9x ( y<x Æ Eyx Æ
9y ( x<y Æ Exy Æ 9x ( y<x Æ Eyx ).
This sentence expresses "there is an increasing path of length 5". Its variable complexity is 2.
Variable complexity 2 means we can rewrite the formula using only 2 distinct variables (quantified multiple times). Hence, FOm is called the m-variable fragment of first-order logic.
Bounded Variable Logics
The variable hierarchy refers to
FO1 ½ FO2 ½ ½ FOm ½ where FO = m¸1 FOm.
Question: Is the variable hierarchy strict/non-collapsing (in terms of expressive power) on a given class of structures?
The answer is YES on the class of all structures (or all finite structures): "the universe contains at least m elements" is expressible in FOm but not FOm-1.
Bounded Variable Logics
Some collapse results: FO ´ FO2 on finite linear orders. FO ´ FO3 on finite linear orders with any
number of unary relations [Poizat '82]. Question: What about ordered graphs
(= finite simple graphs with a linear order)? k-CLIQUE is a natural candidate property for
proving the separation FOk-1 < FOk on ordered graphs.
Bounded Variable Logics
One can show 3-CLIQUE is not definable in FO2 by playing the 2-pebble Ehrenfeucht-Fraisse game on (seq. of) ordered graphs:
It had been an open question (since [Immerman '82]) whether FO ´ FO3 on ordered graphs.
G1
G2
Bounded Variable Logics
One can show 3-CLIQUE is not definable in FO2 by playing the 2-pebble Ehrenfeucht-Fraisse game on (seq. of) ordered graphs:
It had been an open question (since [Immerman '82]) whether FO ´ FO3 on ordered graphs.
G1
G2
Games on ordered graphs become difficult with ¸ 3 pebbles/variables.
One explanation may be that every finite ordered graph is defined up to isomorphism by a sentence of FO3.
Bounded Variable Logics
Theorem [R. '08]. k-CLIQUE is not definable in FObk/4c.
Corollary. FObk/4c < FOk on ordered graphs.
Lemma [Immerman '08]. On ordered graphs,
FOk-1 ´ FOk ) FOk ´ FOk+1
(i.e., if the variable hierarchy does not collapse, then it is strict).
Corollary. The variable hierarchy is strict (i.e., FOk < FOk+1) on ordered graphs.
Bounded Variable Logics
Theorem [R. '08]. k-CLIQUE is not definable in FObk/4c.
Corollary. FObk/4c < FOk on ordered graphs.
Lemma [Immerman '08]. On ordered graphs,
FOk-1 ´ FOk ) FOk ´ FOk+1
(i.e., if the variable hierarchy does not collapse, then it is strict).
Corollary. The variable hierarchy is strict (i.e., FOk < FOk+1) on ordered graphs.
These results hold not only for ordered graphs, but for classes of finite graphs with arbitrary numerical predicates (e.g., arithmetic + and £).
Bounded Variable Logics
Theorem [R. '08]. k-CLIQUE is not definable in FObk/4c.
Corollary. FObk/4c < FOk on ordered graphs.
Lemma [Immerman '08]. On ordered graphs,
FOk-1 ´ FOk ) FOk ´ FOk+1
(i.e., if the variable hierarchy does not collapse, then it is strict).
Corollary. The variable hierarchy is strict (i.e., FOk < FOk+1) on ordered graphs.
Moreover, we show that k/4 variables cannot distinguish classes {ordered graphs with no k-cliques} and {ordered graphs with a K-clique} for all k < K.
Circuits
Circuits are comprised of AND, OR, NOT gates (with unbounded fan-in).
We consider polynomial-size, constant-depth (i.e. AC0) circuits with input variables (representing potential edges in an n-vertex graph).
Descriptive Complexity: {m-variable, quantifier-depth d formulas} (on structures with arbitrary
numerical predicates, e.g., linear order)
´ {non-uniform AC0 circuits with size O(nm) and depth d}. [Immerman '82]
AC0 Circuit (in normal form)
Æ
Æ Æ
Ç Ç Ç Ç Ç Ç Ç Ç Ç Ç Ç Ç
ÆÆ Æ Æ Æ Æ
Ç Ç Ç.......
.....
....
Ç
Æ
1,2 n-1,n1,2 1,3 1,3 1,4 1,4 i, j i, j n-1,nn-2,n n-2,n......
size nO(1)
depth O(1)
Ç
AC0 Lower Bound for k-Clique
The k-clique problem (on graphs of size n) requires depth-d circuits of what size?
Trivial upper bound: O(nk) (acheived in depth 2)
J. Lynch '86: (np(k/d3))
P. Beame '90: (nk/89d2)
AC0 Lower Bound for k-Clique
The k-clique problem (on graphs of size n) requires depth-d circuits of what size?
Trivial upper bound: O(nk) (acheived by depth 2)
J. Lynch '86: (np(k/d3))
P. Beame '90: (nk/89d2)
These lower bounds degrade in the exponent of n as the depth d increases, becoming trivial when d > pk.
AC0 Lower Bound for k-Clique
The k-clique problem (on graphs of size n) requires depth-d circuits of what size?
Trivial upper bound: O(nk) (acheived in depth 2)
J. Lynch '86: (np(k/d3))
P. Beame '90: (nk/89d2)
We show: (nk/4)
k-Clique Lower Bound
The k-clique problem (on graphs of size n) requires depth-d circuits of what size?
Trivial upper bound: O(nk) (acheived in depth 2)
J. Lynch '86: (np(k/d3))
P. Beame '90: (nk/89d2)
We show: (nk/4)
We eliminate dependence on d in the exponent of n. Result holds up to depth d = O(plog n) and potentially up to clique-size k = o(log n).
Erdos-Renyi Random Graphs
The random graph ErdosRenyi(n,p),
p = p(n) 2 [0,1], has n vertices. Edges are independently included with probability p.
We parameterize p(n) = n– where > 0 (so small ! large p ! denser graph).
= 2/(k-1) is the threshold for the appearance of k-cliques. That is, ER(n,n-) almost surely has k-cliques if < 2/(k-1). ER(n,n-) almost surely has no k-clique if > 2/(k-1).
ErdosRenyi(n,p)
n = 50
p = n–0.68
(p is above the threshold
n-2/3 for the appearance
of 4-cliques)
Erdos-Renyi Random Graphs
A Bit of Notation
Notation 1. For an ordered graph G = hV, E, <i and a set A µ V, let
G[A] = G [ (clique on A) = hV, E [ , <i.
Notation 2. For ordered graphs G and H,
, G and H satisfy the same FOm
sentences of quantifier-depth d
, 9 winning strategy for Duplicator in
the d-round m-pebble game on G,H
HG md
Random Graph + Random Cliques
Let G = Erdos-Renyi(n,1/2) with linear order. Let e = random 2-element subset of {1,...,n}.
Theorem. [Ajtai, Furst-Saxe-Sipser, Hastad, Boppana, ...]
For every (quantifier-depth) d,
almost surely as n ! 1.
NB. This is a disguised way of saying that
AC0 functions have average sensitivity no(1).
G[e]G d
Random Graph + Random Cliques
Let G = Erdos-Renyi(n,n –
2/(k
–
1.5)) with lin. order.
NB. With high probability, G has no k-clique
but n(1) many (k–1)-cliques.
X µ {1,...,n} random of size |X| = k–1.
Obs 1. For every (quantifier-depth) d,
asymptotically almost surelyG[X]G d
Random Graph + Random Cliques
Let G = Erdos-Renyi(n,n –
2/(k
–
1.5)) with lin. order.
NB. With high probability, G has no k-clique
but n(1) many (k–1)-cliques.
X, A µ {1,...,n} random of size |X| = k–1, |A| = k.
Obs 1. For every (quantifier-depth) d,
asymptotically almost surely
Obs 2. a.a.s.
G[X]G d
G[A]G k/
Random Graph + Random Cliques
Let G = Erdos-Renyi(n,n –
2/(k
–
1.5)) with lin. order.
NB. With high probability, G has no k-clique
but n(1) many (k–1)-cliques.
X, A µ {1,...,n} random of size |X| = k–1, |A| = k.
Obs 1. For every (quantifier-depth) d,
asymptotically almost surely
Obs 2. a.a.s.
Main Theorem. For every (quantifier-depth) d,
a.a.s.
G[X]G d
G[A]G k/
G[A]G k/4d
Theorem. G = Erdos-Renyi(n, n–2/(k–1.5)), |A| = k.
Then a.a.s. for every d.G[A]G k/4d
Proof Idea. For each k/4-tuple of vertices v = (v1,...,vk/4), we
identify a small subset B(v) µ A (of size · k/2, but empty for most v).
Key Property: For every B(v) µ C µ A such that |C| · k/2,
hG[B(v)], vi ´d hG[C], vi.
Theorem. G = Erdos-Renyi(n, n–2/(k–1.5)), |A| = k.
Then a.a.s. for every d.G[A]G k/4d
Proof Idea. For each k/4-tuple of vertices v = (v1,...,vk/4), we
identify a small subset B(v) µ A (of size · k/2, but empty for most v).
Key Property: For every B(v) µ C µ A such that |C| · k/2,
hG[B(v)], vi ´d hG[C], vi.
In other words: Once you add the clique on B(v) to the pebbled
structure hG, vi, adding a larger subclique of A up to size · k/2 does
not change the ´d-theory.
Theorem. G = Erdos-Renyi(n, n–2/(k–1.5)), |A| = k.
Then a.a.s. for every d.G[A]G k/4d
Proof Idea. For each k/4-tuple of vertices v = (v1,...,vk/4), we
identify a small subset B(v) µ A (of size · k/2, but empty for most v).
Key Property: For every B(v) µ C µ A such that |C| · k/2,
hG[B(v)], vi ´d hG[C], vi.
Duplicator's Strategy: "Mentally substitute" the pebbled structure hG[B(v)], vi for hG[A], vi.
Theorem. G = Erdos-Renyi(n, n–2/(k–1.5)), |A| = k.
Then a.a.s. for every d.
Def. Call v = (v1,...,vk/4), w = (w1,...,wk/4) 2 [n]k/4
neighbors if vi wi for exactly one i 2 {1,...,k/4}.
Suppose we could show (with high probability):
There exist sets B(v) µ A for all v 2 [n]k/4 s.t.
1. B(u) = ; for some u,
2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all nbrs v, w.
Then the conclusion follows easily!
G[A]G k/4d
G[A]G k/4d
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3 u1 u2 u30123456
G G[A]Round
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
u1 u2 u3
x u2 u3
0123456
G G[A]Round
Duplicator matches Spoilerexactly in Round 1.
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
u1 u2 u3
x u2 u3
0123456
G G[A]Round hG[B(u1,u2,u3)], x, u2, u3i´d hG[B(x,u2,u3)], x, u2, u3i
We have:
• G = G[B(u1,u2,u3)] by (1)
• ´d above by (3)
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
u1 u2 u3
x u2 u3
0123456
G G[A]Round
Suppose Spoiler movespebble 2 to y in G.
hG[B(u1,u2,u3)], x, u2, u3i´d hG[B(x,u2,u3)], x, u2, u3i
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
u1 u2 u3
x u2 u3
0123456
G G[A]Round hG[B(u1,u2,u3)], x, y, u3i´d-1 hG[B(x,u2,u3)], x, y', u3i
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Duplicator plays an auxiliary game to
find vertex y'.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
u1 u2 u3
x u2 u3
x y' u3
0123456
G G[A]Round hG[B(u1,u2,u3)], x, y, u3i´d-1 hG[B(x,u2,u3)], x, y', u3i
Duplicator replies by movingpebble 2 to y' in G[A].
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
u1 u2 u3
x u2 u3
x y' u3
0123456
G G[A]RoundhG[B(u1,u2,u3)], x,
y, u3i´d-1 hG[B(x,u2,u3)], x, y', u3i´d hG[B(x,y',u3)], x, y', u3i
assumption (3)
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
u1 u2 u3
x u2 u3
x y' u3
x y' z''
0123456
G G[A]RoundhG[B(u1,u2,u3)], x,
y, u3i´d-1 hG[B(x,u2,u3)], x, y', u3i´d hG[B(x,y',u3)], x, y', u3i
Suppose Spoiler movespebble 3 to z'' in G[A].
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
u1 u2 u3
x u2 u3
x y' u3
x y' z''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
x, y, z i´d-2 hG[B(x,u2,u3)], x, y', z' i´d-1 hG[B(x,y',u3)], x, y', z''i
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Duplicator plays two auxiliary games to
find vertex z.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
u1 u2 u3
x u2 u3
x y' u3
x y' z''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
x, y, z i´d-2 hG[B(x,u2,u3)], x, y', z' i´d-1 hG[B(x,y',u3)], x, y', z'' i
Duplicator replies by movingpebble 1 to z in G.
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
u1 u2 u3
x u2 u3
x y' u3
x y' z''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
x, y, z i´d-2 hG[B(x,u2,u3)], x, y', z' i´d-1 hG[B(x,y',u3)], x, y', z'' i´d hG[B(x,y',z'')], x, y', z'' i
assumption (3)
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
t y z
u1 u2 u3
x u2 u3
x y' u3
x y' z''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
x, y, z i´d-2 hG[B(x,u2,u3)], x, y', z' i´d-1 hG[B(x,y',u3)], x, y', z'' i´d hG[B(x,y',z'')], x, y', z'' i
Spoiler
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
t y z
u1 u2 u3
x u2 u3
x y' u3
x y' z''
t''' y' z''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
t, y, z i´d-3 hG[B(x,u2,u3)], t', y', z' i´d-2 hG[B(x,y',u3)], t'',y', z'' i´d-1 hG[B(x,y',z'')], t''',y',z'' i´d hG[B(t''',y',z'')], t''',y',z'' i
Duplicator
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
t y z
u1 u2 u3
x u2 u3
x y' u3
x y' z''
t''' y' z''
t''' y' j''''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
t, y, z i´d-3 hG[B(x,u2,u3)], t', y', z' i´d-2 hG[B(x,y',u3)], t'',y', z'' i´d-1 hG[B(x,y',z'')], t''',y',z'' i´d hG[B(t''',y',z'')], t''',y',z'' i
Spoiler
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
t y z
t y j
u1 u2 u3
x u2 u3
x y' u3
x y' z''
t''' y' z''
t''' y' j''''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
t, y, j i´d-4 hG[B(x,u2,u3)], t', y', j' i´d-3 hG[B(x,y',u3)], t'',y', j'' i´d-2 hG[B(x,y',z'')], t''',y',j''' i´d-1 hG[B(t''',y',z'')], t''',y',j'''' i´d hG[B(t''',y',j'''')], t''',y',j'''' i
Duplicator
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
t y z
t y j
u1 u2 u3
x u2 u3
x y' u3
x y' z''
t''' y' z''
t''' y' j''''
0123456
G G[A]RoundhG[B(u1,u2,u3)],
t, y, j i´d-4 hG[B(x,u2,u3)], t', y', j' i´d-3 hG[B(x,y',u3)], t'',y', j'' i´d-2 hG[B(x,y',z'')], t''',y',j''' i´d-1 hG[B(t''',y',z'')], t''',y',j'''' i´d hG[B(t''',y',j'''')], t''',y',j'''' i
Auxiliary games on G + various small cliques give strategy for G versus G + large clique
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Winning strategy for Duplicator in hG, ui hG[A], ui (k=12)
k/4d
u1 u2 u3
x u2 u3
x y u3
x y z
t y z
t y jet cet era
u1 u2 u3
x u2 u3
x y' u3
x y' z''
t''' y' z''
t''' y' j''''
et cet era
0123456
G G[A]Round...and so on, for d rounds.
NB. Assumption (2) implies this is a winning strategy for Duplicator (i.e., quantifier-free types are preserved in each round).
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Alas, this assumption is too strong!
We cannot actually find sets B(v) µ A, v 2 [n]k/4, meeting these conditions.
Assume: There exist sets B(v) µ A, v 2 [n]k/4, such that
1. B(u) = ; for some u, 2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´d hG[B(w)], vi for all neighbors v, w.
Alas, this assumption is too strong!
We cannot actually find sets B(v) µ A, v 2 [n]k/4, meeting these conditions.
So we weaken this assumption (by simply restricting the notion of neighboring tuples).
The weaker assumption will still imply G[A]G k/4d
What actually works
For a sufficiently small constant > 0, we fix an arbitrary tree T with vertices 1,...,n and degree n and diameter 2/ (plus all self-loops).
Call v = (v1,...,vm), w = (w1,...,wm) 2 [n]m T-neighbors if 9i 2 {1,...,m} such that (vi,wi) is an edge of T and vj = wj for all j i.
T-guarded d-round Ehrenfeucht-Fraisse game on ordered graphs H1,H2 with vertex set [n]:
Spoiler and Duplicator are constrained to move along edges of T. (Notation. )2dT,1 HH
We are able to show:
Lemma. W.h.p. 9 sets B(v) µ A, v 2 [n]k/4, s.t.
1. B(u) = ; for some u,
2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´T, 2d/ hG[B(w)], vi for all
T-neighbors v and w.
It follows that (by same argument).
We conclude that since 2/ = Diam(T)
T-guarded rounds simulate one unguarded round.
G[A]G k/42d/ T,
G[A]G k/4d
What actually works
We are able to show:
Lemma. W.h.p. 9 sets B(v) µ A, v 2 [n]k/4, s.t.
1. B(u) = ; for some u,
2. B(v) ¶ A Å {v1,...,vk/4} for all v,
3. hG[B(v)], vi ´T, 2d/ hG[B(w)], vi for all
T-neighbors v and w.
It follows that (by same argument).
We conclude that since 2/ = Diam(T)
T-guarded rounds simulate one unguarded round.
G[A]G k/42d/ T,
G[A]G k/4d
What actually works
These sets B(v) have a nifty definition!
Main challenge: Proving 8v, |B(v)| · k/2 with high probability.
Arguments involve Hastad's Switching Lemma & new results on randomly restrictly AC0 functions.
Main Theorem (circuit version)
Let > 0 and k 2 N.
Suppose C is a sequence of constant-depth circuits of size O(n(1+)/2) on inputs.
Then almost surely C has the same value on
1. a random graph G = ErdosRenyi(n,n –
),
2. the graph G [ (random k-clique).
Full Sensitivity
A Boolean function
f : n ! is fully sensitive if it depends on all n input bits.
Formally: for every i 2 [n] = {1,...,n}, there exists x 2 {0,1}n such that
f(x) f(x with ith bit flipped).
Random Restrictions
Fix a Boolean function
f : n ! A restriction is a function
: n! ¤.That is, for each input bit of f, the restriction either fixes a value (0 or 1) or leaves the input bit "unassigned" (¤).
We can apply to f to get a function
fd : -1¤ ! .
Random Restrictions
Suppose f : n ! is an AC0 function (computed by poly-size constant-depth circuits).
Let : n! ¤ be a random restriction subject to
1. |-1(¤)| = k (for a fixed constant k)
2. Pr[(i) = 1 | (i) ¤] = 1/2
So restriction fd : k ! has k input bits.
Random Restrictions
Suppose f : n ! is an AC0 function (computed by poly-size constant-depth circuits).
Let : n! ¤ be a random restriction subject to
1. |-1(¤)| = k (for a fixed constant k)
2. Pr[(i) = 1 | (i) ¤] = 1/2
So restriction fd : k ! has k input bits.
Lemma 1.
Pr[fd is fully sensitive] · 1/nk - o(1)
Random Restrictions
Suppose f : n ! is an AC0 function (computed by poly-size constant-depth circuits).
Let : n! ¤ be a random restriction subject to
1. |-1(¤)| = k (for a fixed constant k)
2. Pr[(i) = 1 | (i) ¤] = 1/2.
So restriction fd : k ! has k input bits.
Lemma 1.
Pr[fd is fully sensitive] · 1/nk - o(1)
Proof uses a standard application of Hastad's Switching Lemma.
In the special case k = 1, lemma says that AC0 functions have average sensitivity no(1).
Lemma 1. Pr[fd is fully sensitive] · 1/nk - o(1)
Proof.
Consider an AC0 circuit computing f.
f
Fix small > 0 and hit f with a random restriction
: [n] ! {0,1,¤} such that Pr[(i) = ¤] = n–
Pr[(i) = 1 | (i) ¤] = 1/2
Lemma 1. Pr[fd is fully sensitive] · 1/nk - o(1)
Proof.
fd
Fix small > 0 and hit f with a random restriction
: [n] ! {0,1,¤} such that Pr[(i) = ¤] = n–
Pr[(i) = 1 | (i) ¤] = 1/2
expected size n1-
¤0 0 ¤¤¤¤¤1 1 0 1 1 0 ¤1 0 ¤¤¤¤¤1 0 1 1 0 0 1 0 1 0 ¤
Lemma 1. Pr[fd is fully sensitive] · 1/nk - o(1)
Proof.
fd
expected size n1-
¤0 0 ¤¤¤¤¤1 1 0 1 1 0 ¤1 0 ¤¤¤¤¤1 0 1 1 0 0 1 0 1 0 ¤
Hastad's Switching Lemma ) there is a constant c such that fd is computed by a decision tree of depth c (and hence depends on at most 2c = O(1) inputs) with high probability (better than 1 - 1/nk).
2c
Lemma 1. Pr[fd is fully sensitive] · 1/nk - o(1)
Proof.
expected size n1-
0 0 ¤¤¤1 1 0 1 1 01 0 1 0 1 1 0 0 1 0 1 0
Pick : [n] ! {0,1,¤} by randomly setting all but k variables in -1(¤) to 0 or 1.
2c
1 1 0 1 1 0 0 0 1 0
fd
Assuming fd has a small decision tree, we have
Random Restrictions
Suppose f : n ! is an AC0 function. Let : n! ¤ be a random restriction
subject to
1. |-1(¤)| = k
2. Pr[(i) = 1 | (i) ¤] = 1/2
So restriction fd : k ! has k input bits.
Lemma 1.
Pr[fd is fully sensitive] · 1/nk - o(1)
Random Restrictions
Suppose f : n ! is an AC0 function. Let : n! ¤ be a random restriction
subject to
1. |-1(¤)| = k
2. Pr[(i) = 1 | (i) ¤] = n –
So restriction fd : k ! has k input bits.
Lemma 2.
Pr[fd is fully sensitive] · 1/nk – k – o(1)
Lemma 2. Pr[fd is fully sensitive] · 1/nk – k – o(1)
Proof.
f
Apply Lemma 1 to this modified circuit, which generates an n- biased distribution on n bits from the uniform distribution on n log(n) bits.
Æ
log(n)
ÆÆ ................. ........
Full Vertex Sensitivity
A Boolean function
f : ! (taking graphs as inputs) is fully vertex sensitive if every "vertex" belongs to a sensitive "edge".
Formally: for every i 2 [n], there exist j 2 [n] - {i} and a graph G with vertex set [n] such that
f(G) f(G © {i,j}).
Random Restrictions
Suppose f : ! is an AC0 function. Let : ! ¤ be a random restriction
subject to
-1(¤) µ is a k-clique
2. Pr[(i) = 1 | (i) ¤] = n –
So restriction fd : ! has inputs.
Lemma 3.
Pr[fd is fully vertex sensitive] · 1/nk – – o(1)
Proof. Same idea as in lemmas of 1 and 2.
Random Restrictions
Suppose f : ! is an AC0 function. Let : ! ¤ be a random restriction
subject to
-1(¤) µ is a k-clique
2. Pr[(i) = 1 | (i) ¤] = n –
So restriction fd : ! has inputs.
Lemma 3.
Pr[fd is fully vertex sensitive] · 1/nk – – o(1)
Obs: nk – is roughly the expected number of k-cliques in G(n,n
– ).
Open Questions
Is k-CLIQUE definable in k-1 variables? (We showed that k/4 are required.)
Does k-CLIQUE require constant-depth circuits of size (nk–
) for every ?
Thank you!