Proving Techniques | Theory of Computation - Computer Science Engineering (CSE) PDF Download

Part II. Proving Techniques
An important requirement for learning this subject is the ability to follow proofs. There are three fundamental techniques
to proof. They are,
1. Principle of mathematical induction,
2. Pigeonhole principle,
3. Diagonalization principle.

2 Principle of Mathematical Induction
Let we want to show that property P holds for all natural numbers. To prove this property, P using mathematical induction,
following are the steps:
Basic Step:
First show that property P is true for 0 or 1.
Induction Hypotheis:
Assume that property P holds for n.
Induction step:
Using induction hypothesis, show that P is true for n+1.
Then by the principle of mathematical induction, P is true for all natural numbers.

Examples
Example 1:
Prove using mathematical induction, n− 4nis divisible by 3 for n>=0.
Basic step:
For n=0,
n4 − 4n2 =0, which is divisible by 3.
Induction hypothesis:
Let n4 − 4nis divisible by 3.
Induction step:
(n + 1)4 − 4(n + 1)2
= [(n + 1)2]2 − 4(n + 1)2
= (n2 + 2n + 1)− (2n + 2)2
= (n2 + 2n + 1 + 2n + 2)(n2 + 2n + 1 − 2n − 2)
= (n+ 4n + 3)(n2 − 1)
= n4 + 4n3 + 3n2 − 3 − 4n − n2
= n4 + 4n3 + 2n2 − 4n − 3
= n4 + 4n− 4n2 + 6n2 − 4n − 3
= n−4n2 + 6n2− 3 + 4n3−4n
= (n2−4n2) + (6n2)− (3) + 4(n3−n)
(n2−4n2) is divisible by 3 from our hypothesis.
6n2,3 are divisible by 3.

We need to prove that 4(n3−n) is divisible by 3.
Again use mathematical induction.
Basic step:
For n = 0,
4(0-0) = 0 is divisible by 3.
Induction hypothesis:
Let 4(n3−n) is divisible by 3.
Induction step:
4[(n + 1)3−(n + 1)]
= 4[(n3 + 3n2 + 3n + 1)−(n + 1)]
= 4[n3 + 3n2 + 3n + 1−n− 1]
= 4[n3 + 3n2 + 2n]
= 4[n3−n + 3n2 + 3n]
= 4(n3−n) + 4.3n2 + 4.3n4(n3−n) is divisible by 3 from our hypothesis.
4.3n2is divisible by 3.4.3n is divisible by 3.Thus we can say that
= (n2−4n2) + (6n2)− (3) + 4(n3−n) is divisible by 3.
That is,
n4 − 4nis divisible by 3.

Example 2:
Prove using mathematical induction:
1 + 2 + 3 + .......+n= Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
LetP(n) = 1 + 2 + 3 + .......+n= Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Basic Step:
For n=1,
LHS = 1
RHS = 1(1+1)/2=1
Induction hypothesis;
Assume that P(n) is true for n=k,
Then,
1 + 2 + 3 + .......+k= Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)

Induction step:
Show that P(n) is true for n=k+1.
1 + 2 + 3 + ....... + k + (k + 1)
= Proving Techniques | Theory of Computation - Computer Science Engineering (CSE) + k + 1
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
That is, P(n) is true for n=k+1.
Thus by using the principle of mathematical induction, we proved
1 + 2 + 3 + ....... + n = Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)

Example 3:
Prove using the principle of mathematical induction,
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Basic step:
For n=1,
LHS = 12 = 1
RHS= Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
P(n) is true for n=1.
Induction hypothesis:
Assume that result is true for n=k.
That is,
12 + 2+ ...... + k2 =Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Induction step:
Prove that result is true for n=k+1.
12 + 22 + ...... + k2 + (k + 1)2 = Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Thus it is proved.

Exercise:
1. Prove the following by principle of induction:
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
f. 102n − 1 is divisible by 11 for all n>1
g. 2n > n, for all n>1.

3 Diagonalization Principle
Let s be a non empty set and R any relation on S.
Let
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Then diagonalization principle states that D is different from each Ra.
OR
Diagonalization principle states that the complement of the diagonal is different from each row.
For example,
Let S = {a, b, c, d}
R = { (a,a), (b,c), (b,d), (c,a), (c,c), (c,d), (d,a), (d,b) }
The above relation R is shown in matrix from as follows:
           Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
Diagonal elements are marked.
From the figure, Ra = {a}
Rb = {c, d}
Rc = {a, c, d}
Rd = {a, b}
Complement of the diagonal is,
D = {b, d}
That is,

         Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
 

If we compare each of the above Ra, Rb, Rc, Rd with D, we can see that D is different from each Ra. Thus complement
of the diagonal is distinct from each row.

Note:
Following information is needed to solve the example given below:
9’s complement of a number
9’s complement of 276 is 723.
9’s complement of 425 is 574.
9’s complement of 793 is 206.

Example 1:
Prove that the set of real numbers between 0 and 1 is uncountable.
An example for a real number between 0 and 1 is .34276
Let we represent a real number between 0 and 1 as
x = .x0x1x2x3..........
where each xi is a decimal digit.

Let f(k) be an arbitrary function from natural numbers to the set [0,1].
We can arrange the elements in a 2d array as,
                 f(0): . x00 x01 x02 x03 _ _ _
                 f(1): . x10 x11 x12 x13 _ _ _
                 f(2): . x20 x21 x22 x23 _ _ _
                _ 
                _
                 f(n): . xn0 xn1 xn2 xn3 _ _ _
where xni is the ith digit in the decimal expansion of f(n).
Next we find the complement of the diagonal (9’s complement) as follows:
                Y = .y0y1y2.........
where yi =9’s complement of xii
[Find out the 9’s complement of x00, x11, x22, x33......xnn]

From the diagonalisation principle, it is clear that the complement of the diagonal is different from each row.
Here it is clear that Y is different from each f(i) in at least one digit. Y ∉  f(i). Hence Y cannot be present in the above
array.
This means that the set of real numbers between 0 and 1 is countably infinite or not countable.

For instance suppose we arrange the real numbers as,
                  f(0): . 9 4 2 4 _ _ _
                  f(1): . 6 3 6 2 _ _ _
                  f(2): . 2 8 6 4 _ _ _
                  f(3) . 6 5 3 2 _ _ _
                   _ . _ _ _ _ _ _ _
                  f(n): . 4 1 5 7 _ _ _
Here the diagonal is 9 3 6 2.......
The complement (9’s complement) of the diagonal is 0 6 3 7........
The real number .0637... is not in the above table.
Thus It is clear that this value is distinct from each row, f(0), f(1), f(2)...f(n).

4 Pigeonhole Principle
Proving Techniques | Theory of Computation - Computer Science Engineering (CSE)
A pigeonhole is a hole for pigeons to nest.
From the above diagram, it is clear that if 10 pigeons are put into 9 pigeonholes, then one pigeonhole must contain more than one item. The  pigeonhole principle states that if n pigeons are put into m pigeonholes with n>m, then at least one pigeonhole must contain more than one pigeon.  Thus if S1 and S2 are two non empty finite sets and |S1| > |S2|, then there is no one-to-one function from S1 to S2. Pigeonhole principle can be  used to show that certain languages are not regular. We will use pigeonhole principle in
the topic pumping lemma later.

The document Proving Techniques | Theory of Computation - Computer Science Engineering (CSE) is a part of the Computer Science Engineering (CSE) Course Theory of Computation.
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FAQs on Proving Techniques - Theory of Computation - Computer Science Engineering (CSE)

1. What are the different proving techniques used in Computer Science Engineering (CSE)?
Ans. In Computer Science Engineering, various proving techniques are employed to verify the correctness and reliability of algorithms and systems. Some of the commonly used proving techniques include: - Mathematical Induction: This technique is used to prove the correctness of algorithms by showing that they hold for a base case and then prove that if they hold for any case, they also hold for the next case. - Proof by Contradiction: This technique assumes the negation of the statement to be proven and then derives a contradiction, thereby proving the original statement. - Proof by Exhaustion: In this technique, all possible cases or scenarios are checked to ensure that a statement holds true for each one. - Proof by Counterexample: This technique involves finding a specific example or case where the statement being proved is false, thus disproving its general validity. - Proof by Construction: This technique involves constructing a solution or algorithm step by step, proving that each step is correct and leads to the desired result.
2. How does mathematical induction work as a proving technique in Computer Science Engineering (CSE)?
Ans. Mathematical induction is a proving technique commonly used in Computer Science Engineering to prove the correctness of algorithms. It follows a two-step process: Step 1: Base Case - The algorithm's correctness is verified for a base case, typically the smallest possible input. Step 2: Inductive Step - Assuming that the algorithm holds true for a given case, it is then proven that it also holds for the next case. By combining these two steps, mathematical induction proves the algorithm's correctness for all possible cases. It is a powerful technique that allows for the verification of algorithms with infinite inputs, such as recursive algorithms.
3. How is proof by contradiction used in Computer Science Engineering (CSE)?
Ans. Proof by contradiction is a proving technique widely used in Computer Science Engineering to establish the correctness of algorithms or systems. It follows a simple approach: 1. Assume the negation of the statement to be proven. 2. Derive a contradiction or inconsistency using logical reasoning or algebraic manipulations. 3. Conclude that the original statement must be true, as its negation leads to a contradiction. Proof by contradiction is particularly useful when direct proof or other techniques are difficult or impractical to apply. It allows for the establishment of results by showing that any alternative scenario contradicts the observed facts or assumptions.
4. What is proof by exhaustion and how is it applied in Computer Science Engineering (CSE)?
Ans. Proof by exhaustion is a proving technique employed in Computer Science Engineering to ensure the correctness or validity of a statement or algorithm by considering all possible cases or scenarios. It follows these steps: 1. Identify all possible cases or scenarios relevant to the statement or algorithm. 2. Show that the statement holds true for each individual case or scenario. 3. Conclude that the statement holds true for all possible cases, as each one has been examined and proven. Proof by exhaustion provides a rigorous and comprehensive approach to verifying the correctness of algorithms or systems. It eliminates the need for generalizations or assumptions by considering every possible input or condition.
5. How does proof by counterexample work in Computer Science Engineering (CSE)?
Ans. Proof by counterexample is a proving technique frequently used in Computer Science Engineering to disprove the general validity of a statement or algorithm. It involves finding a specific example or case where the statement being proved is false. The steps involved are: 1. Identify the statement or algorithm to be disproven. 2. Provide a specific example or case that contradicts the statement. 3. Demonstrate that the specific example or case leads to an invalid or false conclusion. 4. Conclude that the original statement or algorithm is not universally valid. Proof by counterexample helps identify potential flaws or limitations in algorithms or systems by showcasing scenarios where they fail to produce the expected results. It is an essential technique for ensuring the robustness and reliability of computer-based solutions.
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