algorithms

Figure 8.7 Reductions between search problems.
All of NP
SAT
3SAT
INDEPENDENT SET
3D MATCHING
VERTEX COVER
CLIQUE
ZOE
SUBSET SUM
ILP
RUDRATA CYCLE
TSP
8.3 The reductions
We shall now see that the search problems of Section 8.1 can be reduced to one another as
depicted in Figure 8.7. As a consequence, they are all NP-complete.
Before we tackle the specific reductions in the tree, let’s warm up by relating two versions
of the Rudrata problem.
RUDRATA (s, t)-PATH−→RUDRATA CYCLE
Recall the RUDRATA CYCLE problem: given a graph, is there a cycle that passes through each
vertex exactly once? We can also formulate the closely related RUDRATA (s, t)-PATH problem,
in which two vertices s and t are specified, and we want a path starting at s and ending at t
that goes through each vertex exactly once. Is it possible that RUDRATA CYCLE is easier than
RUDRATA (s, t)-PATH? We will show by a reduction that the answer is no.
The reduction maps an instance (G = (V, E), s, t) of RUDRATA (s, t)-PATH into an instance
G ′ = (V ′ , E ′ ) of RUDRATA CYCLE as follows: G ′ is simply G with an additional vertex x and
two new edges {s, x} and {x, t}. For instance:
G G ′
s
s
t
x
t
So V ′ = V ∪ {x}, and E ′ = E ∪ {{s, x}, {x, t}}. How do we recover a Rudrata (s, t)-path in G
given any Rudrata cycle in G ′ ? Easy, we just delete the edges {s, x} and {x, t} from the cycle.
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