Randomised Enumeration of Small Witnesses Using a Decision Oracle
Many combinatorial problems involve determining whether a universe of n elements contains a witness consisting of k elements which have some specified property. In this paper we investigate the relationship between the decision and enumeration versions of such problems: efficient methods are known f...
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| Published in | Algorithmica Vol. 81; no. 2; pp. 519 - 540 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
15.02.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0178-4617 1432-0541 1432-0541 |
| DOI | 10.1007/s00453-018-0404-y |
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| Abstract | Many combinatorial problems involve determining whether a universe of
n
elements contains a witness consisting of
k
elements which have some specified property. In this paper we investigate the relationship between the decision and enumeration versions of such problems: efficient methods are known for transforming a decision algorithm into a search procedure that finds a single witness, but even finding a second witness is not so straightforward in general. We show that, if the decision version of the problem can be solved in time
f
(
k
)
·
p
o
l
y
(
n
)
, there is a randomised algorithm which enumerates all witnesses in time
e
k
+
o
(
k
)
·
f
(
k
)
·
p
o
l
y
(
n
)
·
N
, where
N
is the total number of witnesses. If the decision version of the problem is solved by a randomised algorithm which may return false negatives, then the same method allows us to output a list of witnesses in which any given witness will be included with high probability. The enumeration algorithm also gives rise to an efficient algorithm to count the total number of witnesses when this number is small. |
|---|---|
| AbstractList | Many combinatorial problems involve determining whether a universe of
n
elements contains a witness consisting of
k
elements which have some specified property. In this paper we investigate the relationship between the decision and enumeration versions of such problems: efficient methods are known for transforming a decision algorithm into a search procedure that finds a single witness, but even finding a second witness is not so straightforward in general. We show that, if the decision version of the problem can be solved in time
f
(
k
)
·
p
o
l
y
(
n
)
, there is a randomised algorithm which enumerates all witnesses in time
e
k
+
o
(
k
)
·
f
(
k
)
·
p
o
l
y
(
n
)
·
N
, where
N
is the total number of witnesses. If the decision version of the problem is solved by a randomised algorithm which may return false negatives, then the same method allows us to output a list of witnesses in which any given witness will be included with high probability. The enumeration algorithm also gives rise to an efficient algorithm to count the total number of witnesses when this number is small. Many combinatorial problems involve determining whether a universe of n elements contains a witness consisting of k elements which have some specified property. In this paper we investigate the relationship between the decision and enumeration versions of such problems: efficient methods are known for transforming a decision algorithm into a search procedure that finds a single witness, but even finding a second witness is not so straightforward in general. We show that, if the decision version of the problem can be solved in time f(k)·poly(n), there is a randomised algorithm which enumerates all witnesses in time ek+o(k)·f(k)·poly(n)·N, where N is the total number of witnesses. If the decision version of the problem is solved by a randomised algorithm which may return false negatives, then the same method allows us to output a list of witnesses in which any given witness will be included with high probability. The enumeration algorithm also gives rise to an efficient algorithm to count the total number of witnesses when this number is small. |
| Author | Meeks, Kitty |
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| Keywords | Enumeration algorithms Randomized algorithms Parameterized complexity Approximate enumeration Color coding |
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n
elements contains a witness consisting of
k
elements which have some specified... Many combinatorial problems involve determining whether a universe of n elements contains a witness consisting of k elements which have some specified... |
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| SubjectTerms | Algorithm Analysis and Problem Complexity Algorithms Combinatorial analysis Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Enumeration Mathematics of Computing Randomization Theory of Computation Universe |
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| Title | Randomised Enumeration of Small Witnesses Using a Decision Oracle |
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