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Not-all-equal 3-satisfiability

Not-all-equal 3-satisfiability is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Not-all-equal 3-satisfiability rather than just read about it. In short: In computational complexity, not-all-equal 3-satisfiability (NAE3SAT) is an NP-complete variant of the Boolean satisfiability problem, often used in proofs of NP-completeness. Definition Like 3-satisfiability, an instance of the problem consists of a collection of Boolean variables and a collection of clauses, each of which combines three variables or negations of variables.

Not-all-equal 3-satisfiability — main illustration
Not-all-equal 3-satisfiability — illustration

Key takeaways

  • Not-all-equal 3-satisfiability belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Not-all-equal 3-satisfiability to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Not-all-equal 3-satisfiability from memory before moving on to harder problems.

Reference excerpt

In computational complexity, not-all-equal 3-satisfiability (NAE3SAT) is an NP-complete variant of the Boolean satisfiability problem, often used in proofs of NP-completeness.

Definition Like 3-satisfiability, an instance of the problem consists of a collection of Boolean variables and a collection of clauses, each of which combines three variables or negations of variables. However, unlike 3-satisfiability, which requires each clause to have at least one true Boolean value, NAE3SAT requires that the three values in each clause are not all equal to each other (in other words, at least one is true, and at least one is false).

Hardness

The NP-completeness of NAE3SAT can be proven by a reduction from 3-satisfiability (3SAT). First the nonsymmetric 3SAT is reduced to the symmetric NAE4SAT by adding a common dummy literal s {\displaystyle s} to every clause, then NAE4SAT is reduced to NAE3SAT by splitting clauses as in the reduction of general k {\displaystyle k} -satisfiability to 3SAT. In more detail, a 3SAT instance Φ = ⋀ i = 1 m ( l i , 1 ∨ l i , 2 ∨ l i , 3 ) {\displaystyle \Phi =\bigwedge _{i=1}^{m}(l_{i,1}\vee l_{i,2}\vee l_{i,3})} (where the l i , j {\displaystyle l_{i,j}} are arbitrary literals) is reduced to the NAE4SAT instance Ψ = ⋀ i = 1 m NAE ⁡ ( l i , 1 , l i , 2 , l i , 3 , s ) {\displaystyle \Psi =\bigwedge _{i=1}^{m}\operatorname {NAE} (l_{i,1},l_{i,2},l_{i,3},s)} where s {\displaystyle s} is a new variable. A satisfying assignment for Φ {\displaystyle \Phi } becomes a satisfying assignment for Ψ {\displaystyle \Psi } by setting s = 0 {\displaystyle s=0} . Conversely a satisfying assignment with s = 0 {\displaystyle s=0} for Ψ {\displaystyle \Psi } must have at least one other literal true in each clause and thus be a satisfying assignment for Φ {\displaystyle \Phi } . Finally a satisfying assignment with s = 1 {\displaystyle s=1} for Ψ {\displaystyle \Psi } can because of symmetry of 0 {\displaystyle 0} and 1 {\displaystyle 1} be flipped to produce a satisfying assignment with s = 0 {\displaystyle s=0} . NAE3SAT remains NP-complete when all clauses are monotone (meaning that variables are never negated), by Schaefer's dichotomy theorem. Monotone NAE3SAT can also be interpreted as an instance of the set splitting problem, or as a generalization of graph bipartiteness testing to 3-uniform hypergraphs: it asks whether the vertices of a hypergraph can be colored with two colors so that no hyperedge is monochromatic. More strongly, it is NP-hard to find colorings of 3-uniform hypergraphs with any constant number of colors, even when a 2-coloring exists.

Easy cases Unlike 3SAT, some variants of NAE3SAT in which graphs representing the structure of variables and clauses are planar graphs can be solved in polynomial time. In particular this is true when there exists a planar graph with one vertex per variable, one vertex per clause, an edge for each variable–clause incidence, and a cycle of edges connecting all the variable vertices.

References

Worked examples

Example 1 — a first encounter with Not-all-equal 3-satisfiability

Start with the simplest possible case. Write down what Not-all-equal 3-satisfiability claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Not-all-equal 3-satisfiability before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Not-all-equal 3-satisfiability ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Not-all-equal 3-satisfiability

In research
Not-all-equal 3-satisfiability appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Not-all-equal 3-satisfiability in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Not-all-equal 3-satisfiability is common in secondary-school and first-year university syllabi. It links to neighbouring topics NP-complete problems, Satisfiability problems, so understanding it makes those chapters shorter.
In everyday life
Look for Not-all-equal 3-satisfiability outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Not-all-equal 3-satisfiability in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Not-all-equal 3-satisfiability means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Not-all-equal 3-satisfiability out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Not-all-equal 3-satisfiability in simple terms?

In computational complexity, not-all-equal 3-satisfiability (NAE3SAT) is an NP-complete variant of the Boolean satisfiability problem, often used in proofs of NP-completeness. Definition Like 3-satisfiability, an instance of the problem consists of a collection of Boolean variables and a collection…

Why does Not-all-equal 3-satisfiability matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Not-all-equal 3-satisfiability?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Not-all-equal 3-satisfiability.

Tags

  • NP-complete problems
  • Satisfiability problems

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