1 Introduction
In this note, we first establish the hardness of the following decision problem.
Definition 1 (3dm).
.
Input: Sets and and a set of matches of size .
Output: YES if there exists such that each element of appears exactly once in . NO otherwise.
To prove that 3DM is NPHard, we reduce an instance of 3SAT to the given problem. Next, we define the 3SAT decision problem.
Definition 2 (3Sat).
.
Input: A boolean formulae in 3CNF form with literals and clauses.
Output: YES if is satisfiable, NO otherwise.
Given an instance of 3SAT with literals and clauses, [1] construct a graph with vertices and edges. Thus, this is a power reduction. In this note, we use a similar but a more efficient gadget and provide a linear time reduction of the 3SAT instance to the given problem.
2 Hardness of 3DM
Theorem 3.
Threedimensional matching is an NPHard problem.
Our reduction is described in Fig. 1. For each literal , let be the number of clauses in which the the literal is present. We construct a “truthsetting” component containing hyperedges (or triangles). We add the following hyperedges to .
Note that one of or have to be selected in a matching . If the former is selected, that corresponds to the variable being assigned true, the latter corresponds to false. This part is the same as the standard construction.
For every clause we add three types of hyperedges. The first type ensures that atleast one of the literals is true.
The other two types of hyperedges (conected to the ’s) say that two of the literals can be either true or false. Hence, we connect them to both and
Note that in the construction refers to the index of the clause in the truthsetting component corresponding to the literal . Using the above construction, we get that
Hence, we see that . Now, . And, we have that . Thus, we see that this construction is linear in the number of clauses.
Now, if the 3SAT formula is satisfiable then there exists a matching for the 3DM problem. If a variable in the assignment then add to else add . For every clause , let (or ) be the variable which is set to true in that clause. Add (or ) to . For the remaining two clauses, add the hyperedges containing and depending upon their assignments. Clearly, is a matching.
Now, the proof for the other direction is similar. If there exists a matching, then one of or have to be selected in a matching . This defines a truth assignment of the variables. Now, the construction of the clause hyperedges ensures that every clause is satisfiable.
3 Exponential Time Hypothesis for 3DM
Before we start the discussion in the section, lets review the definition of the exponential time hypothesis.
Exponential Time Hypothesis (ETH)
There does not exist an algorithm which decides 3SAT and runs in time.
If the exponential hypothesis is true, the standard reduction of 3SAT to 3DM [1] implies that any algorithm for 3DM runs in . However, using the reduction in Section 2, we get a more tighter dependence on stated as a theorem below.
Theorem 4.
If the exponential time hypothesis holds then there does not exist an algorithm which decides the threedimensional matching problem (3DM) and runs in time .
Proof.
For the sake on contradiction, suppose that such an algorithm exists. Then, using the reduction from Section 2 and , we get an algorithm for 3SAT that runs in time which contradicts the ET hypothesis. ∎
An immediate corollary of this result applies to another popular problem Exact Cover by 3sets.
Definition 5 (X3c).
.
Input: . A collections of subsets such that each and contains exactly three elements.
Output: YES if there exist such that each element of occurs exactly once in , NO otherwise.
Corollary 6.
If the exponential time hypothesis holds then there does not exist an algorithm which decides exact cover by 3sets problem (X3C) and runs in time .
Proof.
The proof follows from the trivial reduction of 3DM to X3C where and . ∎
References
 [1] (1979) Computers and intractability. Vol. 174, freeman San Francisco. Cited by: §1, §3.

[2]
(2019)
Semisupervised clustering for deduplication.
In
The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 1618 April 2019, Naha, Okinawa, Japan
, pp. 1659–1667. Cited by: footnote 1.
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