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Nondeterministic extensions of the StrongExponential Time Hypothesis andconsequences for non-reducibility
Marco L. Carmosino, Jiawei Gao, Russell Impagliazzo, IvanMikhailin, Ramamohan Paturi, Stefan Schneider
UCSD
2015
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Fine-Grained Hardness
For many problems progress has stalled:
Edit Distance in Ω(n2) [WagnerFischer74]3-SUM in Ω(n2)CKT-SAT in Ω(2n)3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reachGoal: Explain bounds from common principle
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Fine-Grained Hardness
For many problems progress has stalled:Edit Distance in Ω(n2) [WagnerFischer74]
3-SUM in Ω(n2)CKT-SAT in Ω(2n)3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reachGoal: Explain bounds from common principle
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Fine-Grained Hardness
For many problems progress has stalled:Edit Distance in Ω(n2) [WagnerFischer74]3-SUM in Ω(n2)
CKT-SAT in Ω(2n)3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reachGoal: Explain bounds from common principle
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Fine-Grained Hardness
For many problems progress has stalled:Edit Distance in Ω(n2) [WagnerFischer74]3-SUM in Ω(n2)CKT-SAT in Ω(2n)
3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reachGoal: Explain bounds from common principle
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Fine-Grained Hardness
For many problems progress has stalled:Edit Distance in Ω(n2) [WagnerFischer74]3-SUM in Ω(n2)CKT-SAT in Ω(2n)3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reachGoal: Explain bounds from common principle
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Fine-Grained Hardness
For many problems progress has stalled:Edit Distance in Ω(n2) [WagnerFischer74]3-SUM in Ω(n2)CKT-SAT in Ω(2n)3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reach
Goal: Explain bounds from common principle
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Fine-Grained Hardness
For many problems progress has stalled:Edit Distance in Ω(n2) [WagnerFischer74]3-SUM in Ω(n2)CKT-SAT in Ω(2n)3-Points-On-a-Line in Ω(n2)
Proving these lower bounds seems out of reachGoal: Explain bounds from common principle
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Conditional Lower Bounds
Conditional lower bounds are an attempt to tackle theproblem
Relate hard problems by reductions that respect resourcesGoal 1: Explain hardness of as many problems as possibleGoal 2: Explain hardness using a common principle
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Conditional Lower Bounds
Conditional lower bounds are an attempt to tackle theproblemRelate hard problems by reductions that respect resources
Goal 1: Explain hardness of as many problems as possibleGoal 2: Explain hardness using a common principle
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Conditional Lower Bounds
Conditional lower bounds are an attempt to tackle theproblemRelate hard problems by reductions that respect resourcesGoal 1: Explain hardness of as many problems as possible
Goal 2: Explain hardness using a common principle
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Conditional Lower Bounds
Conditional lower bounds are an attempt to tackle theproblemRelate hard problems by reductions that respect resourcesGoal 1: Explain hardness of as many problems as possibleGoal 2: Explain hardness using a common principle
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Important Conjectures: 3-Sum
3-SUM: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0
Simple algorithm: O(n2)
Fastest known algorithms: O(n2/polylog n) [BDP08,GP14]3-SUM conjecture: There is no O(n2−ε) algorithm for3-Sum
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Important Conjectures: 3-Sum
3-SUM: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0
Simple algorithm: O(n2)
Fastest known algorithms: O(n2/polylog n) [BDP08,GP14]3-SUM conjecture: There is no O(n2−ε) algorithm for3-Sum
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Important Conjectures: 3-Sum
3-SUM: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0
Simple algorithm: O(n2)
Fastest known algorithms: O(n2/polylog n) [BDP08,GP14]
3-SUM conjecture: There is no O(n2−ε) algorithm for3-Sum
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Important Conjectures: 3-Sum
3-SUM: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0
Simple algorithm: O(n2)
Fastest known algorithms: O(n2/polylog n) [BDP08,GP14]3-SUM conjecture: There is no O(n2−ε) algorithm for3-Sum
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Important Conjectures: All-Pairs Shortest Path
APSP: Given a weighted directed graph, find the distanceof every pair u, v
Dynamic Programming: O(n3)
Fastest known algorithms: O(n3/2√
log n) [W14]All-pairs shortest path conjecture: There is no O(n3−ε)algorithm for APSP
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Important Conjectures: All-Pairs Shortest Path
APSP: Given a weighted directed graph, find the distanceof every pair u, vDynamic Programming: O(n3)
Fastest known algorithms: O(n3/2√
log n) [W14]All-pairs shortest path conjecture: There is no O(n3−ε)algorithm for APSP
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Important Conjectures: All-Pairs Shortest Path
APSP: Given a weighted directed graph, find the distanceof every pair u, vDynamic Programming: O(n3)
Fastest known algorithms: O(n3/2√
log n) [W14]
All-pairs shortest path conjecture: There is no O(n3−ε)algorithm for APSP
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Important Conjectures: All-Pairs Shortest Path
APSP: Given a weighted directed graph, find the distanceof every pair u, vDynamic Programming: O(n3)
Fastest known algorithms: O(n3/2√
log n) [W14]All-pairs shortest path conjecture: There is no O(n3−ε)algorithm for APSP
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Important Conjectures: Strong Exponential TimeHypothesis
k -SAT: Given a k -CNF, find a satisfying assignment
Brute force: O(2n)
Fastest known algorithm: O(2(1− ck )n) [PPSZ98]
Strong Exponential Time Hypothesis: For every s > 0,there is a k such that k -SAT cannot be solved in time2(1−s)n
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Important Conjectures: Strong Exponential TimeHypothesis
k -SAT: Given a k -CNF, find a satisfying assignmentBrute force: O(2n)
Fastest known algorithm: O(2(1− ck )n) [PPSZ98]
Strong Exponential Time Hypothesis: For every s > 0,there is a k such that k -SAT cannot be solved in time2(1−s)n
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Important Conjectures: Strong Exponential TimeHypothesis
k -SAT: Given a k -CNF, find a satisfying assignmentBrute force: O(2n)
Fastest known algorithm: O(2(1− ck )n) [PPSZ98]
Strong Exponential Time Hypothesis: For every s > 0,there is a k such that k -SAT cannot be solved in time2(1−s)n
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Important Conjectures: Strong Exponential TimeHypothesis
k -SAT: Given a k -CNF, find a satisfying assignmentBrute force: O(2n)
Fastest known algorithm: O(2(1− ck )n) [PPSZ98]
Strong Exponential Time Hypothesis: For every s > 0,there is a k such that k -SAT cannot be solved in time2(1−s)n
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Conditional Lower Bounds
SETH
3-SUM
APSP
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
Single-Source FlowTriangle Collection
...
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
Single-Source FlowTriangle Collection
...
Other Problems
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
Single-Source FlowTriangle Collection
...
Other Problems
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
Single-Source FlowTriangle Collection
...
Other Problems
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Conditional Lower Bounds
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
Single-Source FlowTriangle Collection
...
Max FlowMin-Cost Max-Flow
Hitting SetFirst-Order Properties
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Fine-Grained Reductions
Formalized in [VWW10]
Fine-grained reduction from (L1,T1) to (L2,T2)
Turing reduction that respects resources
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Fine-Grained Reductions
Formalized in [VWW10]Fine-grained reduction from (L1,T1) to (L2,T2)
Turing reduction that respects resources
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Fine-Grained Reductions
Formalized in [VWW10]Fine-grained reduction from (L1,T1) to (L2,T2)
Turing reduction that respects resources
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Fine-Grained Reduction
L1
x
0/1
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Fine-Grained Reduction
L1
x
0/1
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Fine-Grained Reduction
L1
x
0/1
L2
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Fine-Grained Reduction
L1
x
0/1
L2
L2
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Fine-Grained Reduction
L1
x
0/1
T2(n1)1−δ
T2(n3)1−δ
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Fine-Grained Reduction
L1
x
0/1
T2(n1)1−δ
T2(n3)1−δ
T1(n)1−ε
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Fine-Grained Reduction
L1
x
0/1
T2(n1)1−δ
T2(n3)1−δ
T1(n)1−ε
T1(n)1−ε
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Fine-Grained Reduction
L1
x
0/1
T2(n1)1−δ
T2(n3)1−δ
T1(n)1−ε
T1(n)1−ε
∑i T2(ni)
1−δ
≤ T1(n)1−ε
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Proof Idea
Lemma (Basic Idea)Every SETH-hard problem has property X. 3-SUM and APSPdo not have property X.
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NSETH
Nondeterministic Strong Exponential Time HypothesisFor every s > 0, there is a k such that k -SAT cannot be solvedin co-nondeterministic time 2(1−s)n
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Property X
Det. Nondet. Co-Nondet. X ExampleT T 1−ε T yes CNF-SATT T T 1−ε yes DNF-TAUTT T T yes Exact-Max-SATT T 1−ε T 1−ε no 3-SUM
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Property X
LemmaAssuming NSETH, any problem that is SETH-hard with time Tunder deterministic reductions either
Cannot be solved in nondeterministic time T (1−s)
Cannot be solved in co-nondeterministic time T (1−s)
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Property X
L1
x ∈ L1
0/1
L2
L2
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Property X
L1
x ∈ L1
0/1
L2
L2
Nondeterministicbits
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Property X
L1
x ∈ L1
0/1
L2T2(n3)1−δ
L2T2(n3)1−δ
T1(n)1−ε Nondeterministicbits
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Property X
L1
x 6∈ L1
0/1
L2T2(n3)1−δ
L2T2(n3)1−δ
T1(n)1−ε Nondeterministicbits
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Outline
¬NSETH implies interesting circuit lower bounds
Problems with fast nondeterministic andco-nondeterministic algorithms (¬X )First-order graph properties
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Outline
¬NSETH implies interesting circuit lower boundsProblems with fast nondeterministic andco-nondeterministic algorithms (¬X )
First-order graph properties
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Outline
¬NSETH implies interesting circuit lower boundsProblems with fast nondeterministic andco-nondeterministic algorithms (¬X )First-order graph properties
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¬SETH and ¬NSETH Imply Circuit Lower Bounds
The work of [JVM13] uses a two-part strategy to show¬SETH =⇒ Circuit Lower Bounds:
1 A tight implication from C-CKT-SAT algorithms to C lowerbounds
2 Decomposition of C-circuits into∨
CNF form
“¬NSETH =⇒ Circuit Lower Bounds” is implicit in [JVM13],following from their technical contributions and the proofs of[Williams 2013].
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¬SETH and ¬NSETH are Algorithmic
ETH is false: for every ε > 0,3-SAT is in time 2εn
SETH is false: there is a δ < 1 such that for every k , k -SAT
is in time 2δn
NETH is false: for every ε > 0,3-TAUT is innondeterministic time 2εn
NSETH is false: there is a δ < 1 such that for everyk , k -TAUT is in nondeterministic time 2δn
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¬SETH and ¬NSETH are Algorithmic
ETH is false: for every ε > 0,3-SAT is in time 2εn
SETH is false: there is a δ < 1 such that for every k , k -SAT
is in time 2δn
NETH is false: for every ε > 0,3-TAUT is innondeterministic time 2εn
NSETH is false: there is a δ < 1 such that for everyk , k -TAUT is in nondeterministic time 2δn
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¬SETH and ¬NSETH are Algorithmic
ETH is false: for every ε > 0,3-SAT is in time 2εn
SETH is false: there is a δ < 1 such that for every k , k -SAT
is in time 2δn
NETH is false: for every ε > 0,3-TAUT is innondeterministic time 2εn
NSETH is false: there is a δ < 1 such that for everyk , k -TAUT is in nondeterministic time 2δn
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¬SETH and ¬NSETH are Algorithmic
ETH is false: for every ε > 0,3-SAT is in time 2εn
SETH is false: there is a δ < 1 such that for every k , k -SAT
is in time 2δn
NETH is false: for every ε > 0,3-TAUT is innondeterministic time 2εn
NSETH is false: there is a δ < 1 such that for everyk , k -TAUT is in nondeterministic time 2δn
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Reducing C-CKT-SAT to k -SAT?
For C:
Linear-size circuitsLinear-size series-parallel circuits
There are decompositions: ∀C ∈ C, we have C =∨
CNFk .Execute the faster k -SAT algorithm on each “leaf” of thedecomposition.
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Outline
The following problems have fast nondeterministic andco-nondeterministic algorithms:
Max-Flow (O(m))Min-Cost Max Flow (O(m))3-SUM (O(n3/2))All-Pairs Shortest Path (O(n2.69))
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Outline
The following problems have fast nondeterministic andco-nondeterministic algorithms:
Max-Flow (O(m))
Min-Cost Max Flow (O(m))3-SUM (O(n3/2))All-Pairs Shortest Path (O(n2.69))
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Outline
The following problems have fast nondeterministic andco-nondeterministic algorithms:
Max-Flow (O(m))Min-Cost Max Flow (O(m))
3-SUM (O(n3/2))All-Pairs Shortest Path (O(n2.69))
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Outline
The following problems have fast nondeterministic andco-nondeterministic algorithms:
Max-Flow (O(m))Min-Cost Max Flow (O(m))3-SUM (O(n3/2))
All-Pairs Shortest Path (O(n2.69))
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Outline
The following problems have fast nondeterministic andco-nondeterministic algorithms:
Max-Flow (O(m))Min-Cost Max Flow (O(m))3-SUM (O(n3/2))All-Pairs Shortest Path (O(n2.69))
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Maximum Flow Problem
s t
3/3
0/2 1/4
2/2
3/3
2/2
2/3
3/3
o
p
q
r
1
1Licensed under CC BY-SA 3.0 via Commons -https://commons.wikimedia.org/wiki/File:Max flow.svg#/media/File:Max flow.svg
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Maximum Flow Problem
Is there a flow of value at least k
Fastest known algorithm: O(mn) [Orlin13]O(m) approximation algorithms for undirected case[KLOS14]Can we prove a Ω(mn) lower bound under SETH?
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Maximum Flow Problem
Is there a flow of value at least kFastest known algorithm: O(mn) [Orlin13]
O(m) approximation algorithms for undirected case[KLOS14]Can we prove a Ω(mn) lower bound under SETH?
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Maximum Flow Problem
Is there a flow of value at least kFastest known algorithm: O(mn) [Orlin13]O(m) approximation algorithms for undirected case[KLOS14]
Can we prove a Ω(mn) lower bound under SETH?
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Maximum Flow Problem
Is there a flow of value at least kFastest known algorithm: O(mn) [Orlin13]O(m) approximation algorithms for undirected case[KLOS14]Can we prove a Ω(mn) lower bound under SETH?
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Maximum Flow Problem
There is a O(m) nondeterministic algorithm
There is a O(m) co-nondeterministic algorithm(Min-cut/Max-flow theorem)Max-Flow is not SETH-hard at time O(m1+ε) underdeterministic reductions (assuming NSETH)Disproving this statement implies new lower bounds forlinear size circuits
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Maximum Flow Problem
There is a O(m) nondeterministic algorithmThere is a O(m) co-nondeterministic algorithm(Min-cut/Max-flow theorem)
Max-Flow is not SETH-hard at time O(m1+ε) underdeterministic reductions (assuming NSETH)Disproving this statement implies new lower bounds forlinear size circuits
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Maximum Flow Problem
There is a O(m) nondeterministic algorithmThere is a O(m) co-nondeterministic algorithm(Min-cut/Max-flow theorem)Max-Flow is not SETH-hard at time O(m1+ε) underdeterministic reductions (assuming NSETH)
Disproving this statement implies new lower bounds forlinear size circuits
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Maximum Flow Problem
There is a O(m) nondeterministic algorithmThere is a O(m) co-nondeterministic algorithm(Min-cut/Max-flow theorem)Max-Flow is not SETH-hard at time O(m1+ε) underdeterministic reductions (assuming NSETH)Disproving this statement implies new lower bounds forlinear size circuits
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Min-Cost Maximum Flow Problem
Edges have a capacity and a cost per unit flow
Is there a flow of value more than k or of value k and costat most c?Fastest algorithm: O(m2) [Orlin88]Special cases: Max-Flow, Min-Cost Perfect BipartiteMatching
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Min-Cost Maximum Flow Problem
Edges have a capacity and a cost per unit flowIs there a flow of value more than k or of value k and costat most c?
Fastest algorithm: O(m2) [Orlin88]Special cases: Max-Flow, Min-Cost Perfect BipartiteMatching
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Min-Cost Maximum Flow Problem
Edges have a capacity and a cost per unit flowIs there a flow of value more than k or of value k and costat most c?Fastest algorithm: O(m2) [Orlin88]
Special cases: Max-Flow, Min-Cost Perfect BipartiteMatching
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Min-Cost Maximum Flow Problem
Edges have a capacity and a cost per unit flowIs there a flow of value more than k or of value k and costat most c?Fastest algorithm: O(m2) [Orlin88]Special cases: Max-Flow, Min-Cost Perfect BipartiteMatching
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Min-Cost Maximum Flow Problem
Simple O(m) nondeterministic algorithm
O(m) co-nondeterministic algorithm based on Klein’s cyclecanceling algorithm:
There is a flow of the same value and smaller cost if andonly if there is a negative cost cycle in the residual graphCo-Nondeterministic O(m) algorithm for negative cycles
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Min-Cost Maximum Flow Problem
Simple O(m) nondeterministic algorithmO(m) co-nondeterministic algorithm based on Klein’s cyclecanceling algorithm:
There is a flow of the same value and smaller cost if andonly if there is a negative cost cycle in the residual graphCo-Nondeterministic O(m) algorithm for negative cycles
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Min-Cost Maximum Flow Problem
Simple O(m) nondeterministic algorithmO(m) co-nondeterministic algorithm based on Klein’s cyclecanceling algorithm:
There is a flow of the same value and smaller cost if andonly if there is a negative cost cycle in the residual graph
Co-Nondeterministic O(m) algorithm for negative cycles
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Min-Cost Maximum Flow Problem
Simple O(m) nondeterministic algorithmO(m) co-nondeterministic algorithm based on Klein’s cyclecanceling algorithm:
There is a flow of the same value and smaller cost if andonly if there is a negative cost cycle in the residual graphCo-Nondeterministic O(m) algorithm for negative cycles
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Certifying Negative Cycles
Co-nondeterministic algorithm follows from propertiesBellman-Ford algorithm
Potential: p : V → R such that for all(u, v) ∈ Ep(u) + l(u, v) ≥ p(v)
There is a negative weight cycle if and only if there is nopotential
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Certifying Negative Cycles
Co-nondeterministic algorithm follows from propertiesBellman-Ford algorithmPotential: p : V → R such that for all(u, v) ∈ Ep(u) + l(u, v) ≥ p(v)
There is a negative weight cycle if and only if there is nopotential
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Certifying Negative Cycles
Co-nondeterministic algorithm follows from propertiesBellman-Ford algorithmPotential: p : V → R such that for all(u, v) ∈ Ep(u) + l(u, v) ≥ p(v)
There is a negative weight cycle if and only if there is nopotential
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Min-Cost Maximum Flow Problem
Nondeterministically guess the min-cost max flow
Certify that it is a max flow by guessing a cutCertify that it is minimum cost by guessing a potentialTime complexity: O(m)
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Min-Cost Maximum Flow Problem
Nondeterministically guess the min-cost max flowCertify that it is a max flow by guessing a cut
Certify that it is minimum cost by guessing a potentialTime complexity: O(m)
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Min-Cost Maximum Flow Problem
Nondeterministically guess the min-cost max flowCertify that it is a max flow by guessing a cutCertify that it is minimum cost by guessing a potential
Time complexity: O(m)
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Min-Cost Maximum Flow Problem
Nondeterministically guess the min-cost max flowCertify that it is a max flow by guessing a cutCertify that it is minimum cost by guessing a potentialTime complexity: O(m)
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Min-Cost Maximum Flow Problem
Min-Cost Max-Flow is not SETH-hard at time O(m1+ε)under deterministic reductions (assuming NSETH)
Disproving this statement implies new lower bounds forlinear size circuits
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Min-Cost Maximum Flow Problem
Min-Cost Max-Flow is not SETH-hard at time O(m1+ε)under deterministic reductions (assuming NSETH)Disproving this statement implies new lower bounds forlinear size circuits
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Co-Nondeterministic List and Count Algorithm
Embed original problem into one that is much easier, butmay have some false positive solutions
Nondeterministically list all false positivesCheck that all of them are indeed false positivesCount number of solutions and check that given list iscomplete
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Co-Nondeterministic List and Count Algorithm
Embed original problem into one that is much easier, butmay have some false positive solutionsNondeterministically list all false positives
Check that all of them are indeed false positivesCount number of solutions and check that given list iscomplete
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Co-Nondeterministic List and Count Algorithm
Embed original problem into one that is much easier, butmay have some false positive solutionsNondeterministically list all false positivesCheck that all of them are indeed false positives
Count number of solutions and check that given list iscomplete
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Co-Nondeterministic List and Count Algorithm
Embed original problem into one that is much easier, butmay have some false positive solutionsNondeterministically list all false positivesCheck that all of them are indeed false positivesCount number of solutions and check that given list iscomplete
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3-Sum problem
3-Sum: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0
M = poly(n)
Simple algorithm: O(n2)
FFT based counting algorithm: O(M)
Fast nondeterministic algorithm
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3-Sum problem
3-Sum: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0M = poly(n)
Simple algorithm: O(n2)
FFT based counting algorithm: O(M)
Fast nondeterministic algorithm
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3-Sum problem
3-Sum: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0M = poly(n)
Simple algorithm: O(n2)
FFT based counting algorithm: O(M)
Fast nondeterministic algorithm
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3-Sum problem
3-Sum: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0M = poly(n)
Simple algorithm: O(n2)
FFT based counting algorithm: O(M)
Fast nondeterministic algorithm
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3-Sum problem
3-Sum: Given n integers a1, . . . ,an ∈ [−M,M] find i , j , ksuch that ai + aj + ak = 0M = poly(n)
Simple algorithm: O(n2)
FFT based counting algorithm: O(M)
Fast nondeterministic algorithm
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List and Count Algorithm for 3-SUM
Nondeterministically pick a prime p such that there are tsolutions modulo p
Nondeterministically guess t triples (iq, jq, kq).Check that∀q ≤ t : a[iq] + a[jq] + a[kq] = 0(mod p),buta[iq] + a[jq] + a[kq] 6= 0.Count number of solutions of 3-SUM(mod p) and checkthat it is equal to t .
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List and Count Algorithm for 3-SUM
Nondeterministically pick a prime p such that there are tsolutions modulo pNondeterministically guess t triples (iq, jq, kq).
Check that∀q ≤ t : a[iq] + a[jq] + a[kq] = 0(mod p),buta[iq] + a[jq] + a[kq] 6= 0.Count number of solutions of 3-SUM(mod p) and checkthat it is equal to t .
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List and Count Algorithm for 3-SUM
Nondeterministically pick a prime p such that there are tsolutions modulo pNondeterministically guess t triples (iq, jq, kq).Check that∀q ≤ t : a[iq] + a[jq] + a[kq] = 0(mod p),buta[iq] + a[jq] + a[kq] 6= 0.
Count number of solutions of 3-SUM(mod p) and checkthat it is equal to t .
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List and Count Algorithm for 3-SUM
Nondeterministically pick a prime p such that there are tsolutions modulo pNondeterministically guess t triples (iq, jq, kq).Check that∀q ≤ t : a[iq] + a[jq] + a[kq] = 0(mod p),buta[iq] + a[jq] + a[kq] 6= 0.Count number of solutions of 3-SUM(mod p) and checkthat it is equal to t .
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Analysis
Nondeterministically listing all the false positive can bedone in linear time: O(t)
Counting all the false positive can be done by FFT-basedalgorithm in time O(p)
One can always pick t ,p = O(n3/2)The running time is O(n3/2)
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Analysis
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting all the false positive can be done by FFT-basedalgorithm in time O(p)
One can always pick t ,p = O(n3/2)The running time is O(n3/2)
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Analysis
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting all the false positive can be done by FFT-basedalgorithm in time O(p)
One can always pick t ,p = O(n3/2)The running time is O(n3/2)
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3-SUM modulo p
Consider the first n3/2 primes, ≤ O(n3/2)
ai + aj + ak has at most log(3M) = O(log n) prime factors
On average, a prime p has O(
n3 log(n)n3/2
)= O(n3/2) false
positivesThere is a prime p ≤ O(n3/2) such that t ≤ O(n3/2)solutions
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3-SUM modulo p
Consider the first n3/2 primes, ≤ O(n3/2)
ai + aj + ak has at most log(3M) = O(log n) prime factors
On average, a prime p has O(
n3 log(n)n3/2
)= O(n3/2) false
positivesThere is a prime p ≤ O(n3/2) such that t ≤ O(n3/2)solutions
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3-SUM modulo p
Consider the first n3/2 primes, ≤ O(n3/2)
ai + aj + ak has at most log(3M) = O(log n) prime factors
On average, a prime p has O(
n3 log(n)n3/2
)= O(n3/2) false
positives
There is a prime p ≤ O(n3/2) such that t ≤ O(n3/2)solutions
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3-SUM modulo p
Consider the first n3/2 primes, ≤ O(n3/2)
ai + aj + ak has at most log(3M) = O(log n) prime factors
On average, a prime p has O(
n3 log(n)n3/2
)= O(n3/2) false
positivesThere is a prime p ≤ O(n3/2) such that t ≤ O(n3/2)solutions
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3-SUM
3-SUM is not SETH-hard at time O(n3/2+ε) underdeterministic reductions (assuming NSETH)
Disproving this statement implies new lower bounds forlinear size series-parallel circuits
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3-SUM
3-SUM is not SETH-hard at time O(n3/2+ε) underdeterministic reductions (assuming NSETH)Disproving this statement implies new lower bounds forlinear size series-parallel circuits
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3-SUM
Better co-nondeterministic for 3-SUM will give nontrivialco-nondeterministic for SUBSET SUM. This will prove thatSUBSET SUM is not SETH -hard at time O(2n/2) underdeterministic reductions (assuming NSETH).
Nice coincidence: decision tree complexity of 3-SUM isalso O(n3/2).
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3-SUM
Better co-nondeterministic for 3-SUM will give nontrivialco-nondeterministic for SUBSET SUM. This will prove thatSUBSET SUM is not SETH -hard at time O(2n/2) underdeterministic reductions (assuming NSETH).
Nice coincidence: decision tree complexity of 3-SUM isalso O(n3/2).
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All-Pairs Shortest Path Problem
APSP: Given a weighted directed graph, find the distanceof every pair u, v
Dynamic Programming: O(n3) [FloydWarshall62]We give a co-nondeterministic algorithm for Zero-WeightTriangle
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All-Pairs Shortest Path Problem
APSP: Given a weighted directed graph, find the distanceof every pair u, vDynamic Programming: O(n3) [FloydWarshall62]
We give a co-nondeterministic algorithm for Zero-WeightTriangle
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All-Pairs Shortest Path Problem
APSP: Given a weighted directed graph, find the distanceof every pair u, vDynamic Programming: O(n3) [FloydWarshall62]We give a co-nondeterministic algorithm for Zero-WeightTriangle
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Zero-Weight Triangle
SETH
3-SUM
APSPVector Orthogonality
Edit Distance...
3 Points on LinePolygon Containment
...
Negative TriangleGraph Diameter
...
Zero Weight Triangle
Single-Source FlowTriangle Collection
...
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Zero-Weight Triangle
ZWT: Given a weighted graph with all weights ∈ [−M,M],find a triangle of total weight equal to 0.
Trivial algorithm : O(n3)
ZWT modulo p in time O(pnω)
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Zero-Weight Triangle
ZWT: Given a weighted graph with all weights ∈ [−M,M],find a triangle of total weight equal to 0.Trivial algorithm : O(n3)
ZWT modulo p in time O(pnω)
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Zero-Weight Triangle
ZWT: Given a weighted graph with all weights ∈ [−M,M],find a triangle of total weight equal to 0.Trivial algorithm : O(n3)
ZWT modulo p in time O(pnω)
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List and Count Algorithm for ZWT
Nondeterministically pick t and a prime p
Nondeterministically guess t triples (xi , yi , zi) of verticesCheck that∀i ≤ t : W [i , j] + W [j , k ] + W [j , i] = 0(mod p),butW [i , j] + W [j , k ] + W [j , i] 6= 0.Count number of solutions of ZWT (mod p) and check thatit is equal to t .
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List and Count Algorithm for ZWT
Nondeterministically pick t and a prime pNondeterministically guess t triples (xi , yi , zi) of vertices
Check that∀i ≤ t : W [i , j] + W [j , k ] + W [j , i] = 0(mod p),butW [i , j] + W [j , k ] + W [j , i] 6= 0.Count number of solutions of ZWT (mod p) and check thatit is equal to t .
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List and Count Algorithm for ZWT
Nondeterministically pick t and a prime pNondeterministically guess t triples (xi , yi , zi) of verticesCheck that∀i ≤ t : W [i , j] + W [j , k ] + W [j , i] = 0(mod p),butW [i , j] + W [j , k ] + W [j , i] 6= 0.
Count number of solutions of ZWT (mod p) and check thatit is equal to t .
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List and Count Algorithm for ZWT
Nondeterministically pick t and a prime pNondeterministically guess t triples (xi , yi , zi) of verticesCheck that∀i ≤ t : W [i , j] + W [j , k ] + W [j , i] = 0(mod p),butW [i , j] + W [j , k ] + W [j , i] 6= 0.Count number of solutions of ZWT (mod p) and check thatit is equal to t .
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Counting Solutions
Define matrix A with A[i , j] = xW [i,j] mod p
Compute A3 in time O(pnω)
For all entries A3[i , i] sum the coefficients of x0, xp, x2p
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Counting Solutions
Define matrix A with A[i , j] = xW [i,j] mod p
Compute A3 in time O(pnω)
For all entries A3[i , i] sum the coefficients of x0, xp, x2p
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Counting Solutions
Define matrix A with A[i , j] = xW [i,j] mod p
Compute A3 in time O(pnω)
For all entries A3[i , i] sum the coefficients of x0, xp, x2p
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]Nondeterministic reduction yields O(n2.69) for APSP
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)
Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]Nondeterministic reduction yields O(n2.69) for APSP
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]Nondeterministic reduction yields O(n2.69) for APSP
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]Nondeterministic reduction yields O(n2.69) for APSP
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]Nondeterministic reduction yields O(n2.69) for APSP
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]
Nondeterministic reduction yields O(n2.69) for APSP
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Analysis
Same argument as for 3-SUM gives t ≤ O(n3
p )
Nondeterministically listing all the false positive can bedone in linear time: O(t)Counting takes time O(pnω)
Pick p = n0.31
The running time is O(n2.69)
It immediately yields O(n2.9) for APSP [VW09]Nondeterministic reduction yields O(n2.69) for APSP
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All-Pairs Shortest Path
All-Pairs Shortest Path is not SETH-hard at time O(n2.69+ε)under deterministic reductions (assuming NSETH)
Disproving this statement implies new lower bounds forlinear size circuits
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All-Pairs Shortest Path
All-Pairs Shortest Path is not SETH-hard at time O(n2.69+ε)under deterministic reductions (assuming NSETH)Disproving this statement implies new lower bounds forlinear size circuits
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Quantifier Structures of Hard Problems
Many SETH-hard problems have similar quantifierstructures:
Orthogonal Vectors (O(n2))(∃v1)(∃v2)(∀i) [(v1[i] = 0) ∨ (v2[i] = 0)]Graph k -Dominating Set (O(nk ))(∃v1) . . . (∃vk )(∀vk+1)
[∨ki=1 E(vi , vk+1)
]Problems with other quantifier structures:
k -Clique (Solvable in time O(nωk/3))(∃v1) . . . (∃vk )
[∧i 6=j E(vi , vj )
]Hitting Set (not known to be SETH-hard)(∃H)(∀S)(∃x) [(u ∈ H) ∧ (u ∈ S)]
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Quantifier Structures of Hard Problems
Many SETH-hard problems have similar quantifierstructures:
Orthogonal Vectors (O(n2))(∃v1)(∃v2)(∀i) [(v1[i] = 0) ∨ (v2[i] = 0)]
Graph k -Dominating Set (O(nk ))(∃v1) . . . (∃vk )(∀vk+1)
[∨ki=1 E(vi , vk+1)
]Problems with other quantifier structures:
k -Clique (Solvable in time O(nωk/3))(∃v1) . . . (∃vk )
[∧i 6=j E(vi , vj )
]Hitting Set (not known to be SETH-hard)(∃H)(∀S)(∃x) [(u ∈ H) ∧ (u ∈ S)]
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Quantifier Structures of Hard Problems
Many SETH-hard problems have similar quantifierstructures:
Orthogonal Vectors (O(n2))(∃v1)(∃v2)(∀i) [(v1[i] = 0) ∨ (v2[i] = 0)]Graph k -Dominating Set (O(nk ))(∃v1) . . . (∃vk )(∀vk+1)
[∨ki=1 E(vi , vk+1)
]
Problems with other quantifier structures:
k -Clique (Solvable in time O(nωk/3))(∃v1) . . . (∃vk )
[∧i 6=j E(vi , vj )
]Hitting Set (not known to be SETH-hard)(∃H)(∀S)(∃x) [(u ∈ H) ∧ (u ∈ S)]
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Quantifier Structures of Hard Problems
Many SETH-hard problems have similar quantifierstructures:
Orthogonal Vectors (O(n2))(∃v1)(∃v2)(∀i) [(v1[i] = 0) ∨ (v2[i] = 0)]Graph k -Dominating Set (O(nk ))(∃v1) . . . (∃vk )(∀vk+1)
[∨ki=1 E(vi , vk+1)
]Problems with other quantifier structures:
k -Clique (Solvable in time O(nωk/3))(∃v1) . . . (∃vk )
[∧i 6=j E(vi , vj )
]Hitting Set (not known to be SETH-hard)(∃H)(∀S)(∃x) [(u ∈ H) ∧ (u ∈ S)]
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Quantifier Structures of Hard Problems
Many SETH-hard problems have similar quantifierstructures:
Orthogonal Vectors (O(n2))(∃v1)(∃v2)(∀i) [(v1[i] = 0) ∨ (v2[i] = 0)]Graph k -Dominating Set (O(nk ))(∃v1) . . . (∃vk )(∀vk+1)
[∨ki=1 E(vi , vk+1)
]Problems with other quantifier structures:
k -Clique (Solvable in time O(nωk/3))(∃v1) . . . (∃vk )
[∧i 6=j E(vi , vj )
]
Hitting Set (not known to be SETH-hard)(∃H)(∀S)(∃x) [(u ∈ H) ∧ (u ∈ S)]
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Quantifier Structures of Hard Problems
Many SETH-hard problems have similar quantifierstructures:
Orthogonal Vectors (O(n2))(∃v1)(∃v2)(∀i) [(v1[i] = 0) ∨ (v2[i] = 0)]Graph k -Dominating Set (O(nk ))(∃v1) . . . (∃vk )(∀vk+1)
[∨ki=1 E(vi , vk+1)
]Problems with other quantifier structures:
k -Clique (Solvable in time O(nωk/3))(∃v1) . . . (∃vk )
[∧i 6=j E(vi , vj )
]Hitting Set (not known to be SETH-hard)(∃H)(∀S)(∃x) [(u ∈ H) ∧ (u ∈ S)]
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.
Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:
All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:
All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.
Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:
All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.
Can be done in O(mk−1)
Our results:
All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
43 / 50
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:
All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
43 / 50
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:
All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
43 / 50
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
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Quantifier Structures of Hard Problems
First-order formula ϕ with k quantifiers
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Each Qi ∈ ∃, ∀.Model checking problem on graphs
Input: Sparse graph G, given by its edge list of size m.Output: Whether G |= ϕ.Can be done in O(mk−1)
Our results:All SETH-hard problems have
Q1 = Q2 = · · · = Qk−1 = ∃,Qk = ∀
If NSETH holds, there is no reduction from this quantifierstructure to other quantifier structures.
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Quantifier Structures of Hard Problems
ϕ = (∃x1)(Q2x2) . . . (Qkxk )ψ
Quantifierstructure
Result Hardness
∃ . . . ∃∀ If solvable in O(mk−1−ε) co-nondeterministic time, thenNSETH is false.
SETH-hard
All k quanti-fiers are ∃’s
solvable in O(mk−1.5) time Easy
More thanone ∀’s
faster co-nondeterministic algo-rithms
Not SETH-hard underNSETH
Exactly one∀, but not atQk
faster co-nondeterministic algo-rithms
Not SETH-hard underNSETH
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .
O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .
O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .
O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .
O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministically
For each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )
If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.
Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]
Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Example: Hitting Set
Decide if ∃S ∀T ∃x (x ∈ S ∧ x ∈ T )
Exactly one ∀ quantifier, but not at Qk .O(m) nondeterministic algorithm
Guess S, enumerate T , guess x .O(m) co-nondeterministic algorithm
Decide if ∀S ∃T ∀x (x /∈ S ∨ x /∈ T ) nondeterministicallyFor each S, guess T , for each (x ∈ S), check if (x ∈ T )If none element x in S is also in T , accept.Otherwise, reject.
Hitting Set ≤FGR Orthogonal Vectors [AVWW16]Orthogonal Vectors 6≤FGR Hitting Set, under NSETH.
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Randomized Reductions
L1
x ∈ L1
0/1
L2
L2
Nondeterministicbits
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Randomized Reductions
The argument does not extend to randomized reductions
Argument only gives a fast Merlin-Arthur algorithm for SATMASETH?There is a O(2n/2) MA algorithm for CNF-SAT [Williams]Zero-error reductions are ok
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Randomized Reductions
The argument does not extend to randomized reductionsArgument only gives a fast Merlin-Arthur algorithm for SAT
MASETH?There is a O(2n/2) MA algorithm for CNF-SAT [Williams]Zero-error reductions are ok
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Randomized Reductions
The argument does not extend to randomized reductionsArgument only gives a fast Merlin-Arthur algorithm for SATMASETH?
There is a O(2n/2) MA algorithm for CNF-SAT [Williams]Zero-error reductions are ok
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Randomized Reductions
The argument does not extend to randomized reductionsArgument only gives a fast Merlin-Arthur algorithm for SATMASETH?There is a O(2n/2) MA algorithm for CNF-SAT [Williams]
Zero-error reductions are ok
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Randomized Reductions
The argument does not extend to randomized reductionsArgument only gives a fast Merlin-Arthur algorithm for SATMASETH?There is a O(2n/2) MA algorithm for CNF-SAT [Williams]Zero-error reductions are ok
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Randomized Reductions
Idea: Consider a stronger hypothesis that rules outrandomized reductions
Non-Uniform Nondeterministic Strong Exponential TimeHypothesisFor every s > 0, there is a k such that k -SAT does not have2(1−s)n size nondeterministic circuits
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Randomized Reductions
Idea: Consider a stronger hypothesis that rules outrandomized reductions
Non-Uniform Nondeterministic Strong Exponential TimeHypothesisFor every s > 0, there is a k such that k -SAT does not have2(1−s)n size nondeterministic circuits
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Summary
Introduced Nondeterministic Strong Exponential TimeHypothesis
If NSETH is false, then new circuit lower bounds followIf NSETH is true, then non-reducibility results followIf there is a deterministic fine-grained reduction from vectororthogonality to hitting set, then we have new lowerbounds for linear size circuits
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Summary
Introduced Nondeterministic Strong Exponential TimeHypothesisIf NSETH is false, then new circuit lower bounds follow
If NSETH is true, then non-reducibility results followIf there is a deterministic fine-grained reduction from vectororthogonality to hitting set, then we have new lowerbounds for linear size circuits
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Summary
Introduced Nondeterministic Strong Exponential TimeHypothesisIf NSETH is false, then new circuit lower bounds followIf NSETH is true, then non-reducibility results follow
If there is a deterministic fine-grained reduction from vectororthogonality to hitting set, then we have new lowerbounds for linear size circuits
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Summary
Introduced Nondeterministic Strong Exponential TimeHypothesisIf NSETH is false, then new circuit lower bounds followIf NSETH is true, then non-reducibility results followIf there is a deterministic fine-grained reduction from vectororthogonality to hitting set, then we have new lowerbounds for linear size circuits
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Open Questions
Deal with randomized reductions
Find exponential time problems not hard under SETH
Adapt the framework to dynamic problemsConsequences of NETH
Every APSP-hard problem has property X, CNFSAT and3-SUM do not
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Open Questions
Deal with randomized reductionsFind exponential time problems not hard under SETH
Adapt the framework to dynamic problemsConsequences of NETH
Every APSP-hard problem has property X, CNFSAT and3-SUM do not
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Open Questions
Deal with randomized reductionsFind exponential time problems not hard under SETH
Adapt the framework to dynamic problems
Consequences of NETH
Every APSP-hard problem has property X, CNFSAT and3-SUM do not
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Open Questions
Deal with randomized reductionsFind exponential time problems not hard under SETH
Adapt the framework to dynamic problemsConsequences of NETH
Every APSP-hard problem has property X, CNFSAT and3-SUM do not
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Open Questions
Deal with randomized reductionsFind exponential time problems not hard under SETH
Adapt the framework to dynamic problemsConsequences of NETH
Every APSP-hard problem has property X, CNFSAT and3-SUM do not
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Thank You!
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