csr2011 june16 17_00_lohrey
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Compressed Membership in Automata withCompressed Labels
Markus LohreyChristian Mathissen
University of Leipzig
June 16, 2011
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 1 / 12
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Motivation
Try to develop algorithms that directly work on compressed data.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 2 / 12
![Page 3: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/3.jpg)
Motivation
Try to develop algorithms that directly work on compressed data.
Goal: Beat straightforward decompress and manipulate strategy.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 2 / 12
![Page 4: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/4.jpg)
Motivation
Try to develop algorithms that directly work on compressed data.
Goal: Beat straightforward decompress and manipulate strategy.
In this talk: focus on compressed strings
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 2 / 12
![Page 5: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/5.jpg)
Motivation
Try to develop algorithms that directly work on compressed data.
Goal: Beat straightforward decompress and manipulate strategy.
In this talk: focus on compressed strings
Applications:
◮ all domains, where massive string data arise and have to beprocessed, e.g. bioinformatics
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 2 / 12
![Page 6: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/6.jpg)
Motivation
Try to develop algorithms that directly work on compressed data.
Goal: Beat straightforward decompress and manipulate strategy.
In this talk: focus on compressed strings
Applications:
◮ all domains, where massive string data arise and have to beprocessed, e.g. bioinformatics
◮ large (and highly compressible) strings often occur as intermediatedata structures (e.g. in computational group theory, program analysis,verification).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 2 / 12
![Page 7: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/7.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 8: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/8.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 9: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/9.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
A straight-line program (SLP) over the alphabet Γ is a sequence ofdefinitions A = (Ai := αi)1≤i≤n, where either αi ∈ Γ or αi = AjAk forsome j , k < i .
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 10: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/10.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
A straight-line program (SLP) over the alphabet Γ is a sequence ofdefinitions A = (Ai := αi)1≤i≤n, where either αi ∈ Γ or αi = AjAk forsome j , k < i .
Alternatively: a context-free grammar that generates exactly one string.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 11: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/11.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
A straight-line program (SLP) over the alphabet Γ is a sequence ofdefinitions A = (Ai := αi)1≤i≤n, where either αi ∈ Γ or αi = AjAk forsome j , k < i .
Alternatively: a context-free grammar that generates exactly one string.
We write val(A) for the unique word generated by A.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 12: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/12.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
A straight-line program (SLP) over the alphabet Γ is a sequence ofdefinitions A = (Ai := αi)1≤i≤n, where either αi ∈ Γ or αi = AjAk forsome j , k < i .
Alternatively: a context-free grammar that generates exactly one string.
We write val(A) for the unique word generated by A.
The size of an SLP A = (Ai := αi)1≤i≤n is |A| = n.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 13: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/13.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
A straight-line program (SLP) over the alphabet Γ is a sequence ofdefinitions A = (Ai := αi)1≤i≤n, where either αi ∈ Γ or αi = AjAk forsome j , k < i .
Alternatively: a context-free grammar that generates exactly one string.
We write val(A) for the unique word generated by A.
The size of an SLP A = (Ai := αi)1≤i≤n is |A| = n.
One may have |val(A)| = 2n.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 14: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/14.jpg)
Straight-line programs
Dictionary-based compression algorithms (LZ77, LZ78) exploit textrepetition.
Straight-line programs are a general representation for compressed strings,which covers most dictionary-based algorithms.
A straight-line program (SLP) over the alphabet Γ is a sequence ofdefinitions A = (Ai := αi)1≤i≤n, where either αi ∈ Γ or αi = AjAk forsome j , k < i .
Alternatively: a context-free grammar that generates exactly one string.
We write val(A) for the unique word generated by A.
The size of an SLP A = (Ai := αi)1≤i≤n is |A| = n.
One may have |val(A)| = 2n.
Thus, an SLP A can be seen as a compressed representation of val(A).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 3 / 12
![Page 15: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/15.jpg)
Straight-line programs
Example: A = (A1 := b, A2 := a, Ai := Ai−1Ai−2 for 3 ≤ i ≤ 7).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 4 / 12
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Straight-line programs
Example: A = (A1 := b, A2 := a, Ai := Ai−1Ai−2 for 3 ≤ i ≤ 7).
Then val(A) = abaababaabaab:
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 4 / 12
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Straight-line programs
Example: A = (A1 := b, A2 := a, Ai := Ai−1Ai−2 for 3 ≤ i ≤ 7).
Then val(A) = abaababaabaab:
A3 = A2A1 = ab
A4 = A3A2 = aba
A5 = A4A3 = abaab
A6 = A5A4 = abaababa
A7 = A6A5 = abaababaabaab
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 4 / 12
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Straight-line programs
Example: A = (A1 := b, A2 := a, Ai := Ai−1Ai−2 for 3 ≤ i ≤ 7).
Then val(A) = abaababaabaab:
A3 = A2A1 = ab
A4 = A3A2 = aba
A5 = A4A3 = abaab
A6 = A5A4 = abaababa
A7 = A6A5 = abaababaabaab
Relationship to dictionary-based compression (Rytter):
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 4 / 12
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Straight-line programs
Example: A = (A1 := b, A2 := a, Ai := Ai−1Ai−2 for 3 ≤ i ≤ 7).
Then val(A) = abaababaabaab:
A3 = A2A1 = ab
A4 = A3A2 = aba
A5 = A4A3 = abaab
A6 = A5A4 = abaababa
A7 = A6A5 = abaababaabaab
Relationship to dictionary-based compression (Rytter):
◮ From an SLP A one can compute in polynomial time LZ77(val(A)).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 4 / 12
![Page 20: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/20.jpg)
Straight-line programs
Example: A = (A1 := b, A2 := a, Ai := Ai−1Ai−2 for 3 ≤ i ≤ 7).
Then val(A) = abaababaabaab:
A3 = A2A1 = ab
A4 = A3A2 = aba
A5 = A4A3 = abaab
A6 = A5A4 = abaababa
A7 = A6A5 = abaababaabaab
Relationship to dictionary-based compression (Rytter):
◮ From an SLP A one can compute in polynomial time LZ77(val(A)).
◮ From LZ77(w) one can compute in polynomial time an SLP A withval(A) = w .
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 4 / 12
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Algorithms on SLP-compressed strings
The mother of all algorithms on SLP -compressed strings:
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 5 / 12
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Algorithms on SLP-compressed strings
The mother of all algorithms on SLP -compressed strings:
Plandowski 1994
The following problem can be solved in polynomial time:
INPUT: SLPs A, B
QUESTION: val(A) = val(B)?
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 5 / 12
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Algorithms on SLP-compressed strings
The mother of all algorithms on SLP -compressed strings:
Plandowski 1994
The following problem can be solved in polynomial time:
INPUT: SLPs A, B
QUESTION: val(A) = val(B)?
Note: The decompress-and-compare strategy does not work here.We cannot compute val(A) and val(B) in polynomial time.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 5 / 12
![Page 24: Csr2011 june16 17_00_lohrey](https://reader033.vdocument.in/reader033/viewer/2022042713/5445e714afaf9fe32a8b46ae/html5/thumbnails/24.jpg)
Algorithms on SLP-compressed strings
The mother of all algorithms on SLP -compressed strings:
Plandowski 1994
The following problem can be solved in polynomial time:
INPUT: SLPs A, B
QUESTION: val(A) = val(B)?
Note: The decompress-and-compare strategy does not work here.We cannot compute val(A) and val(B) in polynomial time.
Plandowski’s algorithm uses combinatorics on words, in particular thePeriodicity-Lemma of Fine and Wilf.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 5 / 12
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Improvements of Plandowski’s result
Gasieniec, Karpinski, Miyazaki, Plandowski, Rytter, Shinohara,Takeda (mid 90’s)
The following problem can be solved in polynomial time(fully compressed pattern matching):
INPUT: SLPs P, T
QUESTION: Is val(P) a factor of val(T), i.e., ∃x , y : val(T) = x val(P) y?
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 6 / 12
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Improvements of Plandowski’s result
Gasieniec, Karpinski, Miyazaki, Plandowski, Rytter, Shinohara,Takeda (mid 90’s)
The following problem can be solved in polynomial time(fully compressed pattern matching):
INPUT: SLPs P, T
QUESTION: Is val(P) a factor of val(T), i.e., ∃x , y : val(T) = x val(P) y?
The best known algorithm has a running time of O(|P| · |T|2)(Lifshits 2006).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 6 / 12
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Generalization: Compressed automata
A compressed automata is an ordinary finite automaton, where everytransition is labelled with an SLP.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 7 / 12
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Generalization: Compressed automata
A compressed automata is an ordinary finite automaton, where everytransition is labelled with an SLP.
Example: The compressed automaton
q qA
Σ Σ
accepts all words that have val(A) as a factor.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 7 / 12
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Generalization: Compressed automata
A compressed automata is an ordinary finite automaton, where everytransition is labelled with an SLP.
Example: The compressed automaton
q qA
Σ Σ
accepts all words that have val(A) as a factor.
The size |A| of the compressed automaton A(with the set ∆ of transition triples) is
|A| =∑
(p,A,q)∈∆
|A|.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 7 / 12
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Compressed membership for compressed automata
Compressed membership for compressed automata:
INPUT: A compressed automaton A and an SLP B.QUESTION: val(B) ∈ L(A)?
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 8 / 12
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Compressed membership for compressed automata
Compressed membership for compressed automata:
INPUT: A compressed automaton A and an SLP B.QUESTION: val(B) ∈ L(A)?
Plandowski, Rytter 1999
◮ Compressed membership for compressed automata belongs toPSPACE.
◮ Compressed membership for compressed automata over a unaryalphabet is NP-complete.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 8 / 12
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Compressed membership for compressed automata
Compressed membership for compressed automata:
INPUT: A compressed automaton A and an SLP B.QUESTION: val(B) ∈ L(A)?
Plandowski, Rytter 1999
◮ Compressed membership for compressed automata belongs toPSPACE.
◮ Compressed membership for compressed automata over a unaryalphabet is NP-complete.
Plandowski and Rytter conjectured that compressed membership forcompressed automata is NP-complete (for every alphabet size).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 8 / 12
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Some combinatorics on words
The period of the word w ∈ Σ∗ is the smallest number p such thatw [k + p] = w [k] for all 1 ≤ k ≤ |w | − p(period(w) = |w | if such a p does not exist).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 9 / 12
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Some combinatorics on words
The period of the word w ∈ Σ∗ is the smallest number p such thatw [k + p] = w [k] for all 1 ≤ k ≤ |w | − p(period(w) = |w | if such a p does not exist).
Let order(w) = ⌊ |w |period(w)⌋.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 9 / 12
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Some combinatorics on words
The period of the word w ∈ Σ∗ is the smallest number p such thatw [k + p] = w [k] for all 1 ≤ k ≤ |w | − p(period(w) = |w | if such a p does not exist).
Let order(w) = ⌊ |w |period(w)⌋.
Example: Let w = abbabbab = (abb)2ab.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 9 / 12
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Some combinatorics on words
The period of the word w ∈ Σ∗ is the smallest number p such thatw [k + p] = w [k] for all 1 ≤ k ≤ |w | − p(period(w) = |w | if such a p does not exist).
Let order(w) = ⌊ |w |period(w)⌋.
Example: Let w = abbabbab = (abb)2ab.
Then period(w) = 3 and order(w) = 2.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 9 / 12
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Some combinatorics on words
The period of the word w ∈ Σ∗ is the smallest number p such thatw [k + p] = w [k] for all 1 ≤ k ≤ |w | − p(period(w) = |w | if such a p does not exist).
Let order(w) = ⌊ |w |period(w)⌋.
Example: Let w = abbabbab = (abb)2ab.
Then period(w) = 3 and order(w) = 2.
For a compressed automaton A, let:
order(A) = max{order(val(A)) | A occurs as a label in A}
period(A) = max{period(val(A)) | A occurs as a label in A}
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 9 / 12
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First main result
Theorem 1
For a compressed automaton A and an SLP B, we can checkval(B) ∈ L(A) in time polynomial in (i) |A|, (ii) |B|, and (iii) order(A).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 10 / 12
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First main result
Theorem 1
For a compressed automaton A and an SLP B, we can checkval(B) ∈ L(A) in time polynomial in (i) |A|, (ii) |B|, and (iii) order(A).
We use the following combinatorial fact:
Let u, v ∈ Σ∗ and let p be a position in v . Then there exist at mostorder(u) many occurrences of u in v that “touch” position p.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 10 / 12
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Second main result
Theorem 2
For a compressed automaton A and an SLP B, we can checkval(B) ∈ L(A) nondeterministically in time polynomial in (i) |A|, (ii) |B|,and (iii) period(A).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 11 / 12
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Second main result
Theorem 2
For a compressed automaton A and an SLP B, we can checkval(B) ∈ L(A) nondeterministically in time polynomial in (i) |A|, (ii) |B|,and (iii) period(A).
Generalizes the result of Plandowski & Rytter for a unary alphabet(NP-completeness of compressed membership for compressed automata).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 11 / 12
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Second main result
Theorem 2
For a compressed automaton A and an SLP B, we can checkval(B) ∈ L(A) nondeterministically in time polynomial in (i) |A|, (ii) |B|,and (iii) period(A).
Generalizes the result of Plandowski & Rytter for a unary alphabet(NP-completeness of compressed membership for compressed automata).
A word w = an has period 1!
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 11 / 12
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Second main result
Theorem 2
For a compressed automaton A and an SLP B, we can checkval(B) ∈ L(A) nondeterministically in time polynomial in (i) |A|, (ii) |B|,and (iii) period(A).
Generalizes the result of Plandowski & Rytter for a unary alphabet(NP-completeness of compressed membership for compressed automata).
A word w = an has period 1!
The theorem is proven by a reduction to the case of a unary alphabet.
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 11 / 12
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Open problems and a conjecture
Conjecture 1
Compressed membership for compressed automata is NP-complete.
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Open problems and a conjecture
Conjecture 1
Compressed membership for compressed automata is NP-complete.
Conjecture 2
If val(B) ∈ L(A) for an SLP B and a compressed automaton A, then thereexists an accepting run of A on val(B) (viewed as a word over the set oftransition triples of A), which can be generated by an SLP of sizepoly(|B|, |A|).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 12 / 12
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Open problems and a conjecture
Conjecture 1
Compressed membership for compressed automata is NP-complete.
Conjecture 2
If val(B) ∈ L(A) for an SLP B and a compressed automaton A, then thereexists an accepting run of A on val(B) (viewed as a word over the set oftransition triples of A), which can be generated by an SLP of sizepoly(|B|, |A|).
Conjecture 2 implies Conjecture 1:
◮ Guess nondeterministically an SLP C of polynomial size.
◮ Check (in deterministic polynomial time), whether val(C) is anaccepting run of A on val(B).
Lohrey, Mathissen (University of Leipzig) Compressed Membership June 16, 2011 12 / 12