多项式空间中几何交图的ETH紧算法研究
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ETH紧算法在多项式空间中的几何交图
多项式空间中几何交图的ETH紧算法是一种高效的算法框架，旨在解决几何图中的NP-complete问题。该框架基于动态规划和加权树深度的概念，能够在多项式空间中解决许多著名的图理论问题，包括独立集、Dominating Set、Cycle Cover、Hamiltonian Cycle、Hamiltonian Path、Steiner Tree、Connected Vertex Cover、Feedback Vertex Set和Odd Cycle Transversal等。
在该框架中，作者引入了加权树深度的概念，以便在几何图中实现高效的算法。该概念基于加权树的概念，旨在将图的结构分解成较小的子图，以便更好地解决问题。通过使用加权树深度，作者能够在多项式空间中解决许多NP-complete问题，而不需要超指数级的空间复杂度。
该框架的主要贡献在于，可以在多项式空间中解决许多著名的图理论问题，而不需要超指数级的空间复杂度。这使得该框架在解决实际问题时具有很高的实用价值。例如，在计算机网络中，独立集问题是一个经典的问题，该框架可以在多项式空间中解决该问题，从而提高计算机网络的效率。
此外，该框架还可以应用于其他领域，例如机器学习、数据挖掘和计算生物学等。这使得该框架具有很高的研究价值和应用前景。
多项式空间中几何交图的ETH紧算法是一种高效的算法框架，能够在多项式空间中解决许多NP-complete问题，具有很高的实用价值和研究价值。
关键词：算法、几何交图、ETH紧算法、多项式空间、加权树深度、图理论、独立集、Dominating Set、Cycle Cover、Hamiltonian Cycle、Hamiltonian Path、Steiner Tree、Connected Vertex Cover、Feedback Vertex Set、Odd Cycle Transversal。
在该框架中，作者还讨论了几何交图的性质和应用，例如在机器学习和数据挖掘中的应用等。这使得该框架具有很高的研究价值和应用前景。
此外，该框架还可以应用于其他领域，例如计算生物学、网络安全等。这使得该框架具有很高的研究价值和应用前景。
多项式空间中几何交图的ETH紧算法是一种高效的算法框架，能够在多项式空间中解决许多NP-complete问题，具有很高的实用价值和研究价值。
Proof. In the following, to avoid cumbersome notation, we assume that the constants in the exponent of
2
O(ω(u))
are 1, i.e., that the algorithm spends 2
ω(u)
· n
O(1)
time at u, and makes 2
ω(u
i
)
recursive calls. It is
easy to see that any other constants in the exponent is absorbed in the exponent of 2
O(n
1−1/d
)
.
Consider node u ∈ V (F ). We will inductively prove that the overall running time of A corresponding to
one recursive call on u is upper bounded by `(u) · n
O(1)
· max
π
u
2
2ω(π
u
)
, where `(u) is the number of leaves in
the subtree of F rooted at u, and the maximum is taken over all root-leaf paths π
u
in the subtree rooted
at u. Assuming this claim is true, then the bound on the running time holds as follows. The running time
of A corresponding to the root r of F is upper bounded by n · n
O(1)
· max
π
2
2ω(π)
, where the maximum
is taken over all root-leaf paths in F . However, 2
2ω(π)
is at most 2
O(wtd(G
P
))
, which is at most 2
O(n
1−1/d
)
.
Furthermore, if the algorithm uses polynomial space at every node in V (F ), and since the (unweighted)
depth of F is at most n, it only uses polynomial space overall.
Now we prove the inductive claim. Fix a node u ∈ V (F ). If u is leaf in the tree, then the running
time is upper bounded by 2
ω(u)
· n
O(1)
by the property of A. Note that `(u) = 1 in this case. Now,
suppose u is an arbitrary internal node in V (F ). The algorithm spends time proportional to 2
ω(u)
· n
O(1)
,
and makes at most 2
ω(u)
recursive calls on the children of u. Let C(u) denote the set of children of u.
By the inductive hypothesis, time taken by A in one recursive call at any child v
i
∈ C(u) is bounded by
`(v
i
) · n
O(1)
· max
π
v
i
2
2ω(π
v
i
)
, where the maximum is taken over all v
i
-leaf paths π
v
i
in the subtree rooted at v
i
.
Therefore, the overall running time at u is bounded by
T (u) ≤ 2
ω(u)
· n
O(1)
+ 2
ω(u)
·
X
v
i
∈C(u)
`(v
i
) · n
O(1)
· max
π
v
i
2
2ω(π
v
i
)
≤ n
O(1)
·
2
ω(u)
+
X
v
i
∈C(u)
`(v
i
) · max
π
v
i
2
ω(u)+2ω(π
v
i
)
≤ `(u) · n
O(1)
·
2
ω(u)
+ max
π
u
2
ω(u)+2ω(π
v
i
)
(Since there are at most `(u) paths π
u
going from u to a leaf)
≤ `(u) · n
O(1)
· max
π
u
2
2ω(π
u
)
.
3 Simple Recursive Algorithms
Independent Set
Recall that the task of the Independent Set problem is, given a graph G, to find an independent set, i.e., a
set of pairwise nonadjacent vertices, of maximum size. Given G = (V, E), we first obtain (G, d, P, G
P
), and
associated (F, ϕ) of weighted treedepth O(n
1−1/d
) in 2
O(n
1−1/d
)
time and polynomial space using Theorem
1 We have the following simple observation from [4].
Observation 1 ([4]). Let P be a κ-partition of G = (V, E), and let I ⊆ V (G) be an independent set. Then,
|I ∩ V
i
| ≤ κ for any V
i
∈ P.
We now describe our recursive algorithm. In addition to (G, d, P, G
P
), and (F, ϕ), the algorithm takes
input u ∈ V (F ), and D ⊆ V (G). Here, u is the current node in V (F ) in the recursive algorithm, and
D ⊆ V (G) denotes the set of disallowed vertices that cannot be added to the independent set, due to
previous choices made by the algorithm.
Let ϕ(u) = V
i
∈ P be the associated part in the κ-partition P, and define I
u
be the collection of subsets
I ⊆ V
i
such that (1) I is independent in G, (2) I ∩ F = ∅, and (3) |I| ≤ κ. For each guess I ∈ I
u
, we call the
algorithm recursively on the children of u, where the set of disallowed nodes is updated to D
0
← D ∪ N
G
(I).
For a child v
j
of u, let MaxIS(v
j
, I) denote the independent set returned by the algorithm corresponding to
guess I. We return the independent set maximizing |I ∪
S
v
j
∈C(u)
MaxIS(v
j
, I)| over all I ∈ I
u
. It is easy
6
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