Challenge Math Challenge - February 2019

  • #51
QuantumQuest said:
Although I think that you can find it easily on the net, in any case, the explicit solution to the Fibonacci recurrence is Fn=1√5(1+√52)n−1√5(1−√52)nF_n = \frac{1}{\sqrt{5}}(\frac{1 + \sqrt{5}}{2})^n - \frac{1}{\sqrt{5}}(\frac{1 - \sqrt{5}}{2})^n known as Binet's formula.
Hi QQ:

I get that the "solution" of the Fibonacci number recursive definition is the closed form formula that calculated the value of the nth number in the series. What I do not get is the relationship of this "solution" to algorithms and the time it takes to perform a calculation using an algorithm. hat I am guessing is there are more than one definition for what it means to solve a recursion expression. One meaning is the closed form that represents the nth term of the recursive sequence, and another meaning has something to do with algorithms and time to perform calculations. If I get the time, I will try to read the 30 page article you cited, and perhaps then I will understand this second meaning.

Regards,
Buzz
 
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  • #52
QuantumQuest said:
For which values of φ\varphi are f⊥gf \perp g in L2([1,∞))?
QuantumQuest said:
Show that there is an integer a∈Za \in \mathbb{Z} such that 64959|(a2−7).
QuantumQuest said:
For how many (a1,…,a6)∈{0,1}6(a_1,\ldots,a_6)\in \{0,1\}^6 is it true that Q(a1ζ+…+a6ζ6)=Q(ζ)?

I am hoping that someone can educate me the meanings of some notation that was not included in any math courses I took as a math major over 50 years ago.
In the first quote, what does the symbol ⊥ mean, and what does L2([1,∞)) mean?
In the second quote, what does the vertical bar in 64959|(a2−7) mean?
In the third quote, what does special font Q mean in Q(a1ζ+…+a6ζ6)=Q(ζ)?

Regards,
Buzz
 
  • #53
@Buzz Bloom I am still concerned by (1-1/n)\ldots (1-(k+1)/n)=1-\frac{1}{n}(1+2+\ldots+ (k+1))+O(1/n^2). I agree that you can expand the lefthand side in powers of 1/n and that the first two coefficients are what you have written. But the coefficients for 1/n^k are going to be polynomials in k and k is not bounded independent of n since k can be as large as n. For example if your next term was k^3/n^2, then for the terms when k is close to n, this is O(n).
 
  • #54
Infrared said:
But the coefficients for 1/nk are going to be polynomials in k and k is not bounded independent of n since k can be as large as n.
Hi Infrared:

Thank you for your reply. You definitely got me. I will have to explore this issue in detail, and it will take quite a while.

Regards,
Buzz
 
  • #55
Buzz Bloom said:
I am hoping that someone can educate me the meanings of some notation that was not included in any math courses I took as a math major over 50 years ago.
In the first quote, what does the symbol ⊥ mean, and what does L2([1,∞)) mean?
In the second quote, what does the vertical bar in 64959|(a2−7) mean?
In the third quote, what does special font Q mean in Q(a1ζ+…+a6ζ6)=Q(ζ)?

Regards,
Buzz
The first two problems aren't mine, but I'm pretty sure the following is intended (and @fresh_42 can clarify what he means if I get it wrong):
L^2([1,∞)) means the set of real-valued functions f defined on [1,\infty) such that \int_1^\infty f(x)^2 dx exists and is finite (technically, it is defined as equivalence classes of such functions up to equality almost everywhere, and the integral means the Lebesgue integral instead of the (improper) Riemann integral, but neither of these facts are important to the problem). There is an inner product on this space defined by
(f,g)=\int_1^\infty f(x)g(x)dx. Then f\perp g means (f,g)=0. Oftentimes, you work with complex-valued functions instead and then the inner product would be (f,g)=\int_1^\infty f(x)\overline{g(x)}dx.

The vertical line means "divides". For example, 3\vert 9 is true but 3\vert 10 is not.

\mathbb{Q} is the field of rational numbers. If \alpha is an element of some field K that contains \mathbb{Q}, then \mathbb{Q}(\alpha) is the smallest subfield of K that contains all elements of \mathbb{Q} as well as \alpha. For example, you can check \mathbb{Q}(\sqrt{2})=\{a+b\sqrt{2}: a,b\in\mathbb{Q}\}.
 
  • #56
Buzz Bloom said:
I get that the "solution" of the Fibonacci number recursive definition is the closed form formula that calculated the value of the nth number in the series. What I do not get is the relationship of this "solution" to algorithms and the time it takes to perform a calculation using an algorithm. hat I am guessing is there are more than one definition for what it means to solve a recursion expression. One meaning is the closed form that represents the nth term of the recursive sequence, and another meaning has something to do with algorithms and time to perform calculations. If I get the time, I will try to read the 30 page article you cited, and perhaps then I will understand this second meaning.

I gave you the (explicit) solution because you asked for the solution of the equation i.e. for the ##F(n)##.

Buzz Bloom said:
Perhaps you can help me gain an insight by advising me regarding a simple recursive function. For this example I have chosen the
Fibonacci numbers as a recursive function.

F(0) = F(1) = 1
For n>1, F(n) = F(n-1) + F(n-2)

What is the solution of this recursive series?

In any case, what you ask in post #50 is something outside the questions ##1## and ##2## of the challenge themselves but I'll try to explain it as it is something inside the context of these questions.

The explicit solution of the Fibonacci series gives you the ##n##-th term of the series. If you want to calculate this by developing / using a program on a computer, you have to implement an algorithm which in this case can be iterative or recursive. The iterative version is based on having a repetition structure (mostly known as a loop) which computes the ##n##-th term of the series in an imperative repetition fashion. Now, the formula ##F_n = F_{n-1} + F_{n-2}## for Fibonacci series, gives us a natural example of recursion which can be written as a recursive function like this

Code:
function fib(n) {
    if n = 0 return 0
    if n = 1 return 1
    return fib(n-1) + fib(n-2)
    }

In order to measure the time it takes for the above algorithm to execute when implemented as a program, approximately, we are measuring in terms of lines of code. Let ##T(n)## denote the number of steps needed to compute fib(n). If ##n \lt 2## the function runs for just a couple of steps i.e. for ##n \leq 1## we have ##T(n) \leq 2##. Now, if ##n## gets larger there are two recursive invocations of the function fib. The time they take is ##T(n -1)## and ##T(n - 2)## respectively. We must also add the time taken for the checks on the value of ##n## and for the final addition. So, we have ##T(n) = T(n - 1) + T(n - 2) + 3## for ##n \gt 1##. Now, if we compare this to the recurrence relation for ##F_n## we easily see that ##T(n) \geq F_n##. ##T(n)## is exponential in ##n##.

In general, any recursive function such as the above gives us a recurrence relation for time: the time for any routine is the time within the routine itself, plus the time for the recursive calls.
 
  • #57
Hi @Infrared and @QuantumQuest:

Thank you both very much for your posts #55 and #56. I feel your posts have taught me some very useful notations and concepts.

Regards,
Buzz
 
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  • #58
Hi @Infrared:

I have fixed another error in post #30, but I still have more work to do on it.

Regarding your issue about O(1/n), I have thought about a method to explain why this concept is correct, but I had not yet added that explanation because I have not yet decided on how to present this concept mathematically in the solution. The concept involves the terms (all multiplied by n) of the form
Gj,k(x,n) = aj,k (1/nj) xk/k! for 1<j<k.​
The limit
limn→∞ nGj,k (x,n) = 0​
because the values of aj,k are all finite and are independent of n. Do you think a detailed mathematical explanation of this would be sufficient to fix the solution?

Regards,
Buzz
 
  • #59
I will solve Problem 4.
It is to find ##L = \lim_{n\to\infty} n ((1 + x/n)^n - e^x)##.

The first term in parentheses, ##(1 + x/n)^n##, is well-known for having ##e^x## as a limit as ##n\to\infty##. So to find L, we must evaluate ##\lim_{n\to\infty} n (\text{that term's difference from } e^x)##. So in order to have a more easily-expanded expression, we do
$$ \left( 1 + \frac{x}{n} \right)^n = \exp \log \left( \left( 1 + \frac{x}{n} \right)^n \right) = \exp \left( n \log \left( 1 + \frac{x}{n} \right) \right) $$
We next expand the logarithm in an infinite series, and then do that to the exponential:
$$ \left( 1 + \frac{x}{n} \right)^n = \exp \left( \sum_{k=1}^\infty \frac{(-1)^{k+1} x^k}{k n^{k-1}} \right) = \exp \left( x - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right) = e^x \left( 1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)$$
Plugging this into the expression for L, we find
$$ L = \lim_{n\to\infty} n e^x \left( 1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) - 1 \right) = \lim_{n\to\infty} e^x \left( - \frac{x^2}{2} + O\left( \frac{1}{n} \right) \right) = - \frac{x^2}{2} e^x$$
Thus, the answer is
$$ L = - \frac{x^2}{2} e^x $$
 
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  • #60
  • #61
lpetrich said:
I will solve Problem 4.
It is to find ##L = \lim_{n\to\infty} n ((1 + x/n)^n - e^x)##.

The first term in parentheses, ##(1 + x/n)^n##, is well-known for having ##e^x## as a limit as ##n\to\infty##. So to find L, we must evaluate ##\lim_{n\to\infty} n (\text{that term's difference from } e^x)##. So in order to have a more easily-expanded expression, we do
$$ \left( 1 + \frac{x}{n} \right)^n = \exp \log \left( \left( 1 + \frac{x}{n} \right)^n \right) = \exp \left( n \log \left( 1 + \frac{x}{n} \right) \right) $$
We next expand the logarithm in an infinite series, and then do that to the exponential:
$$ \left( 1 + \frac{x}{n} \right)^n = \exp \left( \sum_{k=1}^\infty \frac{(-1)^{k+1} x^k}{k n^{k-1}} \right) = \exp \left( x - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right) = e^x \left( 1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)$$
Plugging this into the expression for L, we find
$$ L = \lim_{n\to\infty} n e^x \left( 1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) - 1 \right) = \lim_{n\to\infty} e^x \left( - \frac{x^2}{2} + O\left( \frac{1}{n} \right) \right) = - \frac{x^2}{2} e^x$$
Thus, the answer is
$$ L = - \frac{x^2}{2} e^x $$
Just a little remark:
$$\exp \left( x - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right) = e^x \left( 1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)$$
could have had been a bit more elaborated in my opinion. Obviously we have
$$\exp \left( x - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)=e^x \cdot \exp \left( - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)$$
which raises the question why ##\exp \left( - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)\stackrel{?}{=}\left(1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)##. No big deal, but a little remark how you approximated here, just words, no equations, would have had improved readability a lot in my opinion.
 
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  • #62
I will solve Problem 2.
One must find a function T(n) of n, a function that satisfies the recurrence relation ##T(n) = 2T(n/3) + n## where ##T(1) = 1## and n is a power of 3.

This can be simplified by using the exponent of 3 in each value of n: ##n = 3^k##. So I define a function that is a function of that exponent: ##T(n) = U(\log_3 n)## or ##T(3^k) = U(k)##. Using that definition turns our problem into ##U(k) = 2U(k-1) + 3^k## with ##U(0) = 1##.

This recurrence is inhomogeneous, and one can add a homogeneous recurrence to it: ##U(k) \to U(k) + V(k)##, where ##V(k) = 2V(k-1)##. It is easily verified:
$$ U(k) + V(k) = 2U(k-1) + 3^k + 2V(k) = 2(U(k-1) + V(k-1)) + 3^k $$
The homogeneous part's solution is easy: ##V(k) = 2^k V(0)##. It also is easily verified: ##2V(k-1) = 2 \cdot 2^{k-1} V(0) = 2^k V(0) = V(k)##.

The inhomogeneous part's solution is more difficult. One gets:
$$ U(k) = 2U(k-1) + 3^k = 2(2U(k-1) + 3^{k-1}) + 3^k = 2(2(2U(k-2) + 3^{k-2}) + 3^{k-1}) + 3^k = \dots $$
This suggests a trial solution:
$$ U(k) = \sum_{m=0}^k 2^{k-m} 3^m $$
Inserting it into the recurrence gives
$$ 2 \sum_{m=0}^{k-1} 2^{k-m-1} 3^m + 3^k = \sum_{m=0}^{k-1} 2^{k-m} 3^m + 3^k = \sum_{m=0}^k 2^{k-m} 3^m $$
which demonstrates its correctness. This result can be simplified further. In general,
$$ \sum_{m=0}^k a^{k-m} b^m = a^k \sum_{m=0}^k \left( \frac{b}{a} \right)^m = a^k \frac{1 - (b/a)^{k+1}}{1 - b/a} = \frac{a^{k+1} - b^{k+1}}{a - b}$$
Inserting 3 and 2 gives us ##U(k) = 3^{k+1} - 2^{k+1}##. Combining with the V recurrence gives ##U(k) = 2^k C + 3^{k+1}## for some constant C. It is easily verified:
$$ 2U(k-1) + 3^k = 2(2^{k-1} C + 3^k) + 3^k = 2^k C + (2 + 1) 3^k = 2^k C + 3^{k+1} = U(k) $$
What remains is to solve ##U(0) = 1##. It is ##C + 3 = 1## or ##C = -2##. Thus, the final result is
$$ U(k) = 3^{k+1} - 2^{k+1} $$
or
$$ T(n) = 3n - 2^{\log_3 n + 1} $$
 
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  • #63
I will solve Problem 1.
I will first solve it for the general value of the recurrence ##T(n) = 2T(\lfloor n/2 \rfloor) + 1##, then insert ##T(1) = 4##.

For the recurrence, I will first consider the general case of ##T(n) = F(T(\lfloor n/2 \rfloor))##, and look for patterns in its solution.
  • T(2) = T(3) = F(T(1))
  • T(4) = T(5) = F(T(2)), T(6) = T(7) = F(T(3)) = F(T(2))
  • T(8) = T(9) = F(T(4)), T(10) = T(11) = F(T(5)) = F(T(4)), T(12) = T(13) = F(T(6)) = F(T(4)), T(14) = T(15) = F(T(7)) = F(T(4))
  • ...
This suggests that all values ##T(n)## are equal for ##2^k \leq n \leq 2^{k+1}-1## for each k. To verify this result, I divide this range by 2 and then round down. ##\lfloor 2^k / 2 \rfloor = 2^{k-1}## and ##\lfloor (2^{k+1}-1) / 2 \rfloor = 2^{k-1} - 1##. Thus giving the corresponding range for k-1. Thus,
$$ T(2^k \text{ to } 2^{k+1} - 1) = F(T(2^{k-1} \text{ to } 2^k-1)) $$
with each range's values being equal.

I now change variables for ##T(n)## to get the exponent for 2: ##T(n) = U(\log_2 n)## or ##T(2^k) = U(k)##. The equality over range means that ##T(n) = U(\lfloor \log_2 n \rfloor)##. The recurrence becomes ##U(k) = 2U(k-1) + 1##. Adding 1 to both sides gives ##(U(k) + 1) = 2(U(k-1) + 1)##, giving the solution ##U(k) = 2^k C - 1## for some constant C. It is easy to verify:
$$ 2U(k-1) + 1 = 2(2^{k-1} C - 1) + 1 = 2^k C - 1 = U(k) $$
Since we have a value for T(1), this value gives U(0) = T(1), and the above solution in terms of U(0) is ##U(k) = 2^k (U(0) + 1) - 1##.

Thus,
$$ T(n) = 2^{\lfloor \log_2 n \rfloor} (T(1) + 1) - 1 = 2^{\lfloor \log_2 n \rfloor} \cdot 5 - 1 $$
for T(1) = 4.
 
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  • #64
lpetrich said:
I will solve Problem 2.

In general the way you follow is correct but although I really appreciate your efforts, it is unnecessarily complicated. You can alternatively start from one of the suggested ways in the spoiler (I'm sure you can easily find which way to choose) and go in a much quicker way. In any case, your solution is absolutely acceptable and correct.
Now, in order to be precise - as we are in the context of algorithms for this question, we're interested for big ##O## and big ##\Theta## in comparison with ##T(n)##. So, it would be best to express your result for ##T(n)## as ##T(n) = 3n - 2n^{\log_3 2} \leq 3n - 2n^{0.63} = O(n)##. Now, as ##T(n)## has ##n## as a summation term we immediately see that ##T(n) = \Theta(n)##.
 
  • #65
lpetrich said:
I will solve Problem 1.

Very good.

We can work in the context of algorithms - as it is intended for this question, and use the guess method in a very simple way, which I'll show in order the question to be more clear to all.

The solution we choose to try by guessing in an optimal way is, obviously, of the order ##O(n)##. So, we have to prove that ##T(n) \leq cn## where ##c## is a constant. Substituting in the given recurrence relation and applying strong induction we have ##T(n) = 2c(\lfloor \frac{n}{2} \rfloor) + 1 \leq 2c(\frac{n}{2}) + 1 = cn + 1##.

At this point, we have to point out that the solution we guessed has not yet been proved, as we have not proved that ##T(n) \leq cn## albeit ##cn + 1 = O(n)##. So, we have to modify our guess to ##cn - b## where ##b## is a constant. So, we have ##T(n) \leq 2c(\frac{n}{2}) - 2b + 1 = cn + 1 - 2b \leq cn - b##, for all ##b \geq 1##. Now, because there is the initial condition ##T(1) = 4## we have that it must hold ##T(1) = 4 \leq c\cdot 1 - b## so ##c \geq 4 + b## and because ##b \geq 1## it is ##c \geq 5##. So, we have also found the lower bounds for the values of constants.
 
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  • #66
I will attempt to solve Problem 3.
The problem statement is to find the values of ##\varphi## that make ##f \perp g = \int_1^\infty f(x) g(x) dx = 0## for functions f(x) and g(x):
$$ f(x) = \frac{K_1 x + K_2}{x^2} ,\ g(x) = \frac{K_3 x + K_2}{x^2} $$
where
$$ K_1 = \cos \varphi - \sqrt{3}\sin \varphi + 1 ,\ K_2 = 2\sqrt{3}\sin \varphi ,\ K_3 = \cos \varphi - \sqrt{3}\sin \varphi - 1 $$
Plugging the function expressions into X gives
$$ f \perp g = \int_1^\infty \left( \frac{K_1}{x} + \frac{K_2}{x^2} \right) \left( \frac{K_3}{x} + \frac{K_2}{x^2} \right) dx = \int_1^\infty \left( \frac{K_1 K_3}{x^2} + \frac{(K_1 + K_3)K_2}{x^3} + \frac{(K_2)^2}{x^4} \right) dx$$
Using the well-known integral ## \int_1^\infty x^{-n} \, dx = \frac{1}{1-n} x^{1-n} |_1^\infty = \frac{1}{n-1}##,
$$ f \perp g = (K_1 K_3) + \frac{(K_1 + K_3) K_2}{2} + \frac{(K_2)^2}{3} $$
Inserting the expressions for the K's gives
$$ f \perp g = (\cos^2\varphi - 2\sqrt{3}\cos\varphi\sin\varphi + 3 \sin^2\varphi - 1) + \frac{4\sqrt{3}\cos\varphi\sin\varphi - 12\sin^2\varphi}{2} + \frac{12 \sin^2\varphi}{3} $$
Simplifying further, and using a certain well-known trigonometric identity,
$$ f \perp g = \cos^2\varphi + \sin^2\varphi - 1 = 0 $$

Thus, ##f \perp g = 0## for all ##\varphi##.
 
  • #67
lpetrich said:
I will attempt to solve Problem 3.
The problem statement is to find the values of ##\varphi## that make ##f \perp g = \int_1^\infty f(x) g(x) dx = 0## for functions f(x) and g(x):
$$ f(x) = \frac{K_1 x + K_2}{x^2} ,\ g(x) = \frac{K_3 x + K_2}{x^2} $$
where
$$ K_1 = \cos \varphi - \sqrt{3}\sin \varphi + 1 ,\ K_2 = 2\sqrt{3}\sin \varphi ,\ K_3 = \cos \varphi - \sqrt{3}\sin \varphi - 1 $$
Plugging the function expressions into X gives
$$ f \perp g = \int_1^\infty \left( \frac{K_1}{x} + \frac{K_2}{x^2} \right) \left( \frac{K_3}{x} + \frac{K_2}{x^2} \right) dx = \int_1^\infty \left( \frac{K_1 K_3}{x^2} + \frac{(K_1 + K_3)K_2}{x^3} + \frac{(K_2)^2}{x^4} \right) dx$$
Using the well-known integral ## \int_1^\infty x^{-n} \, dx = \frac{1}{1-n} x^{1-n} |_1^\infty = \frac{1}{n-1}##,
$$ f \perp g = (K_1 K_3) + \frac{(K_1 + K_3) K_2}{2} + \frac{(K_2)^2}{3} $$
Inserting the expressions for the K's gives
$$ f \perp g = (\cos^2\varphi - 2\sqrt{3}\cos\varphi\sin\varphi + 3 \sin^2\varphi - 1) + \frac{4\sqrt{3}\cos\varphi\sin\varphi - 12\sin^2\varphi}{2} + \frac{12 \sin^2\varphi}{3} $$
Simplifying further, and using a certain well-known trigonometric identity,
$$ f \perp g = \cos^2\varphi + \sin^2\varphi - 1 = 0 $$

Thus, ##f \perp g = 0## for all ##\varphi##.
That's correct.

Here is a more elegant solution:

If we define ##p(x)=x^{-1}## and ##q(x)=\sqrt{3}(2-x)x^{-2}##. Then ##\{\,p,q\,\}## define a orthonormal basis of the subspace ##V## they span in ##L^2([1,\infty))##. Now ##f,g## can be written as
$$
f(x)=p(x)+p(x)\cos \varphi + q(x)\sin \varphi \, , \,g(x)=-p(x)+p(x)\cos \varphi +q(x) \sin \varphi
$$
which means they point to the same point on the unit circle of ##V## from the left and from the right intersection with the diameter, and the statement follows by the theorem of Thales, i.e. all values of ##\varphi## fulfill the condition.
 
  • #68
My attempt for High School problem 5:
Any number N when divided by 30 will leave a remainder S such that S∈{0,1,2,3...,29}. The goal is to prove that when N is prime,S is also prime or 1.
Assuming this is not the case and S is not equal to 1 or a prime number, since the prime factors of 30 are 2,3 and 5,and N is prime, then two primes bigger than 5 have to be divisors of S, but this condradicts the fact that s can't be bigger than 29.
As for when dividing any number N by 60, we can use the information that any prime P bigger than 5 can be written as 30x+y, being x a natural number and y a coprime of 30 between 0 and 29, so the possibilities for y are {1,7,11,13,17,19,23,29}, this is true because P is bigger than 5 and prime so it is coprime to 2,3 and 5, so P is coprime to 30, and so is it remainder when divided by 30. For x=0 and x=1 the remainder of the division is y itself, for the primes smaller and equal to 5 {2,3,5} the remainder is 1 since the division can be fatorated. For bigger values of x this doesn't always hold true (ex:109), so doing the same procedure let's say that there is a prime number N that when divided by 60 leaves a remainder y that is not prime or 1.The reaminder in dividing any number by 60 belongs to {0,1,2...,59}, so since the prime factors of 60 are 2,2,3 and 5, and N is prime, two primes bigger than 5 must be divisors of y, and this doesn't contradict the fact that y can't be bigger than 59 since 7x7=49.
 
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  • #69
Leo Consoli said:
My attempt for High School problem 5:
Any number N when divided by 30 will leave a remainder S such that S∈{0,1,2,3...,29}. The goal is to prove that when N is prime,S is also prime or 1.
Assuming this is not the case and S is not equal to 1 or a prime number, since the prime factors of 30 are 2,3 and 5,and N is prime, the remainder S must be equal to the product of 2 prime numbers bigger than 5,...
because if ##2,3## or ##5## divided ##S##, they would divide ##N##, too: ##N=30q +S##. It doesn't have to be a product of exactly two primes, but two primes bigger than ##5## have to be divisors, as per assumption ##S## isn't prime.
... but this contradicts the fact that S can't be bigger than 29.
One can also do it directly: Since ##2,3,5 \nmid S## all remainders which are left are primes or one: ##1,7,11,13,17,19,23,29##. But the indirect method is nicer.
As for when dividing any number N by 60, we can use the information that any prime P bigger than 5 can be written as 30x+y, being x a natural number and y a coprime of 30 between 0 and 29, so the possibilities for y are {1,7,11,13,17,19,23,29}, this is true because P is bigger than 5 and prime so it is coprime to 2,3 and 5, so P is coprime to 30, and so is it remainder when divided by 30. For x=0 and x=1 the remainder of the division is y itself, for the primes smaller and equal to 5 {2,3,5} the remainder is 1 since the division can be fatorated. For bigger values of x this doesn't always hold true (ex:109), so doing the same procedure let's say that there is a prime number N that when divided by 60 leaves a remainder y that is not prime or 1.The reaminder in dividing any number by 60 belongs to {0,1,2...,59}, so since the prime factors of 60 are 2,2,3 and 5, and N is prime, the remainder y must be equal to the product of 2 prime numbers bigger than 5, and this doesn't contradict the fact that y can't be bigger than 59 since 7x7=49.
This is rather complicated and as far as I read it, it only shows that the proof above for ##30## does not work for ##60##. However, this doesn't mean there cannot be another proof which will work! But your example ##109=1\cdot 60 + 49## is the counterexample which is needed to show, that the same statement for ##60## is actually wrong. That a certain proof does not work is meaningless, the example is already the proof that it cannot be proven (by any attempt).
 
  • #70
fresh_42 said:
because if 2,32,32,3 or 555 divided SSS, they would divide NNN, too: N=30q+SN=30q+SN=30q +S. It doesn't have to be a product of exactly two primes, but two primes bigger than 555 have to be divisors, as per assumption SSS isn't prime.
Thank you I will edit it.
fresh_42 said:
One can also do it directly: Since 2,3,5∤S2,3,5∤S2,3,5 \nmid S all remainders which are left are primes or one: 1,7,11,13,17,19,23,291,7,11,13,17,19,23,291,7,11,13,17,19,23,29. But the indirect method is nicer.
I see, I didnt notice this method.
fresh_42 said:
This is rather complicated and as far as I read it, it only shows that the proof above for 303030 does not work for 606060. However, this doesn't mean there cannot be another proof which will work! But your example 109=1⋅60+49109=1⋅60+49109=1\cdot 60 + 49 is the counterexample which is needed to show, that the same statement for 606060 is actually wrong. That a certain proof does not work is meaningless, the example is already the proof that it cannot be proven (by any attempt).
Thank you, if I had thought like this it would have been much easier.
 
  • #71
Holy choo-choo, what kind of high school is this? I can very barely follow these arguments, and I supposedly have a college degree!
 
  • #72
fresh_42 said:
Just a little remark:
$$\exp \left( x - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right) = e^x \left( 1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)$$
could have had been a bit more elaborated in my opinion. Obviously we have
$$\exp \left( x - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)=e^x \cdot \exp \left( - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)$$
which raises the question why ##\exp \left( - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)\stackrel{?}{=}\left(1 - \frac{x^2}{2n} + O\left( \frac{1}{n^2} \right) \right)##. No big deal, but a little remark how you approximated here, just words, no equations, would have had improved readability a lot in my opinion.

I'm very curious about this. I've been pouring over this solution because I think it's REALLY neat. So far, I've been able to follow:

$$ \left( 1+ \frac{x}{n} \right)^{n} = e^{\ln \left( 1+ \frac{x}{n} \right)^{n}} = e^{n \ln \left( 1+ \frac{x}{n} \right)}$$

The series expansion for ##\ln \left( 1+ \frac{x}{n} \right)## is ## \sum_{k=1}^\infty (-1)^{k+1} \frac{x^{k}}{kn^{k}}## so that:
$$n \ln \left( 1+ \frac{x}{n} \right) = \sum_{k=1}^\infty (-1)^{k+1} \frac{x^{k}}{kn^{k-1}} = x- \frac{x^{2}}{2n} + \frac{x^{3}}{3n^{2}} - ... = x- \frac{x^{2}}{2n} + O \left( \frac{1}{n^{2}} \right)$$

Then,
$$\left( 1+ \frac{x}{n} \right)^{n} = e^{\ln \left( 1+ \frac{x}{n}\right)^{n}} = e^{n \ln \left( 1+ \frac{x}{n} \right)} = e^{ \left( x- \frac{x^{2}}{2n} + O \left( \frac{1}{n^{2}} \right) \right)} $$

So, in the parenthesis, we get ## \left( \left( 1 + \frac{x}{n} \right)^{n} - e^{x} \right) = \left( e^{\left( x- \frac{x^{2}}{2n} + O \left( \frac{1}{n^{2}} \right) \right)} - e^{x} \right) = \left( e^{x} e^{- \frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} \right) - e^{x} = e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) ##

Then the whole expression becomes ## \lim_{n \rightarrow \infty} n \left( \left( 1+ \frac{x}{n} \right) ^{n} - e^{x} \right) = \lim_{n \rightarrow \infty} n \left( e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) \right)##How do we get from ## \lim_{n \rightarrow \infty} n \left( e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) \right)## to ## \lim_{n \rightarrow \infty} e^{x} \left( -\frac{x^{2}}{2} + O \left( \frac{1}{n} \right) \right) = -\frac{x^{2}}{2}e^{x}##?
 
  • #73
QuantumP7 said:
I'm very curious about this. I've been pouring over this solution because I think it's REALLY neat. So far, I've been able to follow:

$$ \left( 1+ \frac{x}{n} \right)^{n} = e^{\ln \left( 1+ \frac{x}{n} \right)^{n}} = e^{n \ln \left( 1+ \frac{x}{n} \right)}$$

The series expansion for ##\ln \left( 1+ \frac{x}{n} \right)## is ## \sum_{k=1}^\infty (-1)^{k+1} \frac{x^{k}}{kn^{k}}## so that:
$$n \ln \left( 1+ \frac{x}{n} \right) = \sum_{k=1}^\infty (-1)^{k+1} \frac{x^{k}}{kn^{k-1}} = x- \frac{x^{2}}{2n} + \frac{x^{3}}{3n^{2}} - ... = x- \frac{x^{2}}{2n} + O \left( \frac{1}{n^{2}} \right)$$

Then,
$$\left( 1+ \frac{x}{n} \right)^{n} = e^{\ln \left( 1+ \frac{x}{n}\right)^{n}} = e^{n \ln \left( 1+ \frac{x}{n} \right)} = e^{ \left( x- \frac{x^{2}}{2n} + O \left( \frac{1}{n^{2}} \right) \right)} $$

So, in the parenthesis, we get ## \left( \left( 1 + \frac{x}{n} \right)^{n} - e^{x} \right) = \left( e^{\left( x- \frac{x^{2}}{2n} + O \left( \frac{1}{n^{2}} \right) \right)} - e^{x} \right) = \left( e^{x} e^{- \frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} \right) - e^{x} = e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) ##

Then the whole expression becomes ## \lim_{n \rightarrow \infty} n \left( \left( 1+ \frac{x}{n} \right) ^{n} - e^{x} \right) = \lim_{n \rightarrow \infty} n \left( e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) \right)##How do we get from ## \lim_{n \rightarrow \infty} n \left( e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) \right)## to ## \lim_{n \rightarrow \infty} e^{x} \left( -\frac{x^{2}}{2} + O \left( \frac{1}{n} \right) \right) = -\frac{x^{2}}{2}e^{x}##?
Let ##f(x)=- \frac{x^{2}}{2n}+ O \left( \frac{1}{n^{2}} \right)## for the moment. Then by the series expansion of the exponential function we have ##\exp(f(x))=1+f(x)+\frac{1}{2}f(x)^2 +\ldots## and so
\begin{align*}
n \left( e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) \right)&=e^x \cdot n \cdot \left( e^{f(x)}-1 \right) \\
&= e^x \cdot n \cdot \left( f(x)+ \frac{1}{2}f(x)^2+ \frac{1}{6}f(x)^3+\ldots \right)\\
&= e^x \left\{ n \cdot \left( - \frac{x^2}{2n} + O(n^{-2}) \right) + n \cdot \frac{1}{2} \left( - \frac{x^2}{2n} + O(n^{-2}) \right)^2 + n \cdot g_1(x) \cdot O(n^{-3}) \right\}\\
&= e^x \left\{ -\frac{x^2}{2} +O(n^{-1}) + \frac{1}{2} \cdot \frac{x^4n}{4n^2} + g_2(x) \cdot O(n^{-2}) \right\}\\
&= e^x \left\{ -\frac{x^2}{2} \right\} + g_3(x) \cdot O(n^{-1})\\
& \to e^x \cdot \left( -\frac{x^2}{2} \right)
\end{align*}
with some functions ##g_i(x)## and where we have generously rounded the big-O expressions to ##O(n^{-1})\,.##
 
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  • #74
fresh_42 said:
Let ##f(x)=- \frac{x^{2}}{2n}+ O \left( \frac{1}{n^{2}} \right)## for the moment. Then by the series expansion of the exponential function we have ##\exp(f(x))=1+f(x)+\frac{1}{2}f(x)^2 +\ldots## and so
\begin{align*}
n \left( e^{x} \left( e^{\frac{x^{2}}{2n}} e^{ \left( O \left( \frac{1}{n^{2}} \right) \right)} -1 \right) \right)&=e^x \cdot n \cdot \left( e^{f(x)}-1 \right) \\
&= e^x \cdot n \cdot \left( f(x)+ \frac{1}{2}f(x)^2+ \frac{1}{6}f(x)^3+\ldots \right)\\
&= e^x \left\{ n \cdot \left( - \frac{x^2}{2n} + O(n^{-2}) \right) + n \cdot \frac{1}{2} \left( - \frac{x^2}{2n} + O(n^{-2}) \right)^2 + n \cdot g_1(x) \cdot O(n^{-3}) \right\}\\
&= e^x \left\{ -\frac{x^2}{2} +O(n^{-1}) + \frac{1}{2} \cdot \frac{x^4n}{4n^2} + g_2(x) \cdot O(n^{-2}) \right\}\\
&= e^x \left\{ -\frac{x^2}{2} \right\} + g_3(x) \cdot O(n^{-1})\\
& \to e^x \cdot \left( -\frac{x^2}{2} \right)
\end{align*}
with some functions ##g_i(x)## and where we have generously rounded the big-O expressions to ##O(n^{-1})\,.##

Aha! Thank you SO much! Awesome question and solution!
 
  • #75
QuantumQuest said:
##6##. Let ##f: [0,1] \rightarrow \mathbb{R}## be a continuous function with ##f(0) = f(1)## . Prove that the equation ##f(x) = f(x + \frac{1}{n})## has at least one real root, for all ## n = 1, 2, 3,\dots##

For ##n = 1## the theorem is true, ## x=0## is the solution. Provided that ##n \gt 1## let ##G(x)## be the function defined for the ##\left[0, \frac {n-1} n \right]## interval $$G(x) = f\left(x+ \frac 1 n\right) - f(x).$$ If ##G(0) = 0## then the theorem is proved, ## x=0## is the solution.

If ##G(0) \gt 0##, take the next equations

$$ 0= f(1)-f(0) =\\

f\left(\frac n n\right) - f \left(\frac {n-1} n \right) + f\left(\frac {n-1} n\right)- f\left(\frac {n-2} n\right)+ \dots + f\left(\frac 2 n\right)- f\left(\frac 1 n\right)+ f\left(\frac 1 n\right)- f(0) =

\\ G\left( \frac {n-1} n \right) + G\left( \frac {n-2} {n} \right) + \dots + G\left(\frac 1 n \right) + G(0). $$ Because ##G(0) > 0##, it is necessarily one of the ##G\left( \frac k n \right)## should be negative. But then the ##G(x)## function at the point ## \frac k n ## is negative, at the point ##0## it is positive, so it takes ##0## at an intermediate ##x_0## point according to to the Bolzano-Cauchy intermediate value theorem, where ##f\left(x_0+ \frac 1 n \right) - f(x_0) =0##.

If ##G(0)<0##, we apply the above proof to the ##-f(x)## function.
 
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  • #76
QuantumQuest said:
##8##. In a conference there are ##n## persons. Show that there are two persons such that, from the rest ##n - 2## persons, there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons each of which knows both the aforementioned two persons or else knows no one from them. Suppose that "know someone" is a symmetric relation.
Originally, I interpreted the question as follows

there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons {each of which knows both the aforementioned two persons} or else {each of which knows no one from them}​

but I couldn't solve it.

However, if I interpret it as follows:

there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons each of which {knows both the aforementioned two persons or else knows no one from them}​

then I think I can solve the problem.

------------------------------------------------------------------------------------------------------------------------------------------------

244120


It is a graph with ##n## vertices, in which if two people know each other, the two vertices are bound by a blue edge, if they do not know each other, then yellow edge. At the vertex ##A##, the number of blue edges is ##b_A##, the number of yellow edges is ##y_A##, while in the ##B## the number of blue edges is ##b_B##, the number of yellow edges is ##y_B##.

##\left[ \frac n 2 \right] - 1 \lt \frac {n-1} 2## inequality is true for all positive n.

Suppose that for each vertex ##A## and ##B## of the graph, there are fewer single-colored edge pairs than ##\left[ \frac n 2 \right] - 1 ##, so it is false what the theorem says. In this case, for each pair of vertices ##A## and ##B##, the number of edge pairs of different colors is greater than ##\frac {n-1} 2##. Take one of these ##A## and ##B## pair of vertices.

The following two cases are possible:

(1) ##b_A > \frac {n-1} 2 ## and ## y_B > \frac {n-1} 2##

(2) ##y_A > \frac {n-1} 2 ## and ## b_B > \frac {n-1} 2##​

However, the above is true for vertices ##A## and ##C##, so

(1) if ##b_A > \frac {n-1} 2 ## then ## y_C > \frac {n-1} 2##,

(2) but if ##y_A > \frac {n-1} 2 ## then ## b_C > \frac {n-1} 2##.​

However, with respect to the vertex ##B## and ##C## of the graph, we get a contradiction because in both the previous two cases

(1) if ##b_A > \frac {n-1} 2 ## then ## y_C > \frac {n-1} 2## and ## y_B > \frac {n-1} 2##,

(2) but if ##y_A > \frac {n-1} 2 ## then ## b_C > \frac {n-1} 2## and ## b_B > \frac {n-1} 2##.
ps

I see that I have to write ##\frac {n-2} 2## instead of ##\frac {n-1} 2## above.
 
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  • #77
Periwinkle said:
Originally, I interpreted the question as follows

there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons {each of which knows both the aforementioned two persons} or else {each of which knows no one from them}
but I couldn't solve it.

However, if I interpret it as follows:

there are at least ##\lfloor \frac{n}{2} \rfloor - 1 ## persons each of which {knows both the aforementioned two persons or else knows no one from them}
then I think I can solve the problem.

I cannot really see what you mean here. I think that the wording of the problem is clear:

QuantumQuest said:
Show that there are two persons such that, from the rest ##n - 2## persons, there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons each of which knows both the aforementioned two persons or else knows no one of them

We simply have two persons, the rest remaining after subtracting them from ##n## is ##n-2## and from these we need to show that there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons each of which knows both the two initial persons or knows no one of them.
 
  • #78
Periwinkle said:
I see that I have to write ##\frac {n-2} 2## instead of ##\frac {n-1} 2## above.

True that. EDIT: I refer to ##\left[ \frac n 2 \right] - 1 \lt \frac {n-1} 2## you write. If you mean floor i.e. ##\lfloor \frac{n}{2} \rfloor - 1\lt \frac {n-1} 2## then it holds true and you can also write ##\lfloor \frac{n}{2} \rfloor - 1\leq \frac {n-2} 2##.

Also, I see that you use the symbols "[" and "]" without being double brackets or boldface, so just for avoidance of any confusion, we talk about the greatest integer less than or equal to x i.e. floor(x). I think it's best to use ##\lfloor## and ##\rfloor## instead.
 
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  • #79
QuantumQuest said:
True that. EDIT: I refer to ##\left[ \frac n 2 \right] - 1 \lt \frac {n-1} 2## you write. If you mean floor i.e. ##\lfloor \frac{n}{2} \rfloor - 1\lt \frac {n-1} 2## then it holds true and you can also write ##\lfloor \frac{n}{2} \rfloor - 1\leq \frac {n-2} 2##.

Also, I see that you use the symbols "[" and "]" without being double brackets or boldface, so just for avoidance of any confusion, we talk about the greatest integer less than or equal to x i.e. floor(x). I think it's best to use ##\lfloor## and ##\rfloor## instead.

Thank you very much for your comment. I realized that my proof was completely wrong. Interestingly, proving such an easy-to-understand theorem - at least for me is undoubtedly so difficult.
 
  • #80
Periwinkle said:
Interestingly, proving such an easy-to-understand theorem - at least for me is undoubtedly so difficult.

As a hint, you can start with two persons in the conference and regard a person as "involved" with this couple of persons, if this third person knows exactly one of the persons of the couple. You can generalize this and maybe use A.M. - G.M. along with some combinatorics and see what can it give you.
 
  • #81
QuantumQuest said:
As a hint, you can start with two persons in the conference and regard a person as "involved" with this couple of persons, if this third person knows exactly one of the persons of the couple. You can generalize this and maybe use A.M. - G.M. along with some combinatorics and see what can it give you.

Mark participants in the conference with ##A_1, A_2, \dots A_n##.

Person ##A_i## knows ##k_i## other persons, does not knows ##l_i##. Then it is not involved in ## \binom {k_i} 2 ## pairs because knows both of them, in case of ##\binom {l_i} 2 ## it is not involved, because does not know any of them. Involved in ##k_i \cdot l_i## pairs.

There are a total of ##\binom n 2 ## pairs of persons, for each pair, there are ##n-2## individuals who are either involved or not. $$\binom n 2 (n-2) = \sum_{i=1}^n \left[\binom {k_i} 2 + \binom {l_i} k + k_i \cdot l_i \right] $$ $$ \frac {n(n-1)(n-2)} 2 = -\frac{n(n-1)} 2 + \sum_{i=1}^n \frac {k_i^2+l_i^2+2k_il_i} 2 $$ It follows from arithmetic-geometric inequality that ##k_i^2+l_i^2 \geq 2 k_i l_i##. Therefore $$ \frac {n(n-1)(n-1)} 2 \geq \sum_{i=1}^n 2 k_il_i $$ That is $$ \frac {n(n-1)} 2 \cdot \frac {(n-1)} 2 \geq \sum_{i=1}^n k_il_i $$ If for each person pair, the other ##n-2## people who are not involved are less than ##\left[ \frac n 2 \right]-1##, then the involved is more than ##\frac {n-1} 2##, which is a contradiction.

For integer value function (##[1.2] =1, [1.5] = 1, [1.7] =1##) the following is true:

If ##n=2k##, then ##n-\left( \left[ \frac n 2 \right] -1 \right) = 2k-k+1 = k+1 > k-\frac 1 2 = \frac {n-1} 2##.
If ##n=2k+1##, then ##n-\left(\left[ \frac n 2 \right]-1 \right) = 2k+1-k+1 = k+2 > k = \frac {n-1} 2##
 
  • #82
Periwinkle said:
...Involved in ##k_i \cdot l_i## pairs.

In order to simplify symbols, let's say that a person knows exactly ##k## persons in the conference. Then in how many pairs is he / she involved?

Periwinkle said:
There are a total of ##\binom n 2## pairs of persons, for each pair, there are ##n-2## individuals who are either involved or not.

I think that it would be more useful to think in terms of the combinations of pairs - involved persons.
 
  • #83
QuantumQuest said:
##8##. In a conference there are ##n## persons. Show that there are two persons such that, from the rest ##n - 2## persons, there are at least ##\lfloor \frac{n}{2} \rfloor - 1## persons each of which knows both the aforementioned two persons or else knows no one of them. Suppose that "know someone" is a symmetric relation.

I.

My reasoning above, which I wrote yesterday before and hastened, is based on that
  1. for each ##X## person, we count the number of pairs of individuals for which this ##X## is involved
  2. for each pair of ##X, Y## persons, count the number of other persons involved in this pair.
That is, the number of existing involved relationships is counted in two ways.

(Why is it called "involved"? Because if I know both of the two people at the same time, or I don't know any of them, I will not say to either one that is unfavorable to the other. While in the other case, it can happen, and so I am involved in their relationship.)
245126
1. For each ##A_i## person, we count the number of ##(X, Y)## pairs of individuals for which this ##A_i## is involved and not involved

Mark participants in the conference with ##A_1, A_2, \dots A_n##. Person ##A_i## knows ##k_i## other persons, does not knows ##l_i##. Then it is not involved in ## \binom {k_i} 2 ## pairs because knows both of them, in case of ##\binom {l_i} 2 ## it is not involved, because does not know any of them. Involved in ##k_i \cdot l_i## pairs.

There are a total of ##\binom n 2 ## pairs of persons, for each pair, there are ##n-2## individuals who are either involved or not.

$$\binom n 2 (n-2) = \sum_{i=1}^n \left[\binom {k_i} 2 + \binom {l_i} k + k_i \cdot l_i \right] $$ $$\binom n 2 (n-2) = \sum_{i=1}^n \left[ \frac {k_i(k_i-1)} 2 + \frac {l_i(l_i-1)} 2 + k_i \cdot l_i \right]$$ $$\frac {n(n-1)} 2 (n-2) = \sum_{i=1}^n \frac {{k_i}^2-k_i + {l_i}^2-l_i + 2k_i\cdot l_i} 2 $$ $$\frac {n(n-1)(n-2)} 2 = \sum_{i=1}^n \frac {{k_i}^2+{l_i}^2-(n-1)+ 2k_i\cdot l_i} 2 $$ $$\frac {n(n-1)(n-2)} 2 = -\frac {n(n-1)} 2 + \sum_{i=1}^n \frac {{k_i}^2+{l_i}^2+ 2k_i\cdot l_i} 2 $$ But $$\frac {k_i+l_i} 2 \geq \sqrt{k_il_i}$$ so $$k_i^2+l_i^2\geq2k_il_i$$ Consequently
$$\frac {n(n-1)(n-2)} 2 +\frac {n(n-1)} 2 \geq \sum_{i=1}^n {2k_i\cdot l_i} $$ $$\frac {n(n-1)(n-1)} 2 \geq \sum_{i=1}^n {2k_i\cdot l_i} $$ $$\frac {n(n-1)} 2 \frac {n-1} 2 \geq \sum_{i=1}^n {k_i\cdot l_i} $$ 2. For each pair of ##(X, Y)## persons, count the number of other persons ##A_i## involved in this pair

For all ##n## natural numbers, $$ n- \left(\left[\frac n 2\right]-1 \right) \gt \frac {n-1} 2$$ Suppose the theorem is not true, so for each person pair, the number of people not involved in the pair is less than ##\left[ \frac n 2 \right]-1##. As a result of the above inequality, therefore, for each pair of persons, the number of persons involved is more than ##\frac {n-1} 2##. There are ##\binom n 2## pairs of people, so the total number of involved relationships is over $$\frac {n(n-1)} 2 \cdot \frac{n-1} 2$$ So we got a contradiction because the number of involved relationships should not be more than ##\frac {n(n-1)} 2 \cdot \frac{n-1} 2## and at the same time less than or equal to ##\frac {n(n-1)} 2 \cdot \frac{n-1} 2##.

II.

To prove the inequality of the floor-integer value function

If ##n=2k##, then $$n-\left( \left[ \frac n 2 \right] -1 \right) = 2k-k+1 = k+1 > k-\frac 1 2 = \frac {n-1} 2$$
If ##n=2k+1##, then $$n-\left(\left[ \frac n 2 \right]-1 \right) = 2k+1-k+1 = k+2 > k = \frac {n-1} 2$$

 
  • #84
@Periwinkle, I see a lot of things written but, at times, I can't follow your solution. I asked you to use simpler symbols / notation like denoting with ##k## the number of persons which one person knows in the conference, as a way to simplify things. The question I put

QuantumQuest said:
Then in how many pairs is he / she involved?

was along the lines of a hint as also the second thing I proposed in post #82. In any case, I can't see / understand the sort of contradiction you talk about and where is all this leading to.
 
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  • #85
QuantumQuest said:
@Periwinkle, I see a lot of things written but, at times, I can't follow your solution. I asked you to use simpler symbols / notation like denoting with ##k## the number of persons which one person knows in the conference, as a way to simplify things. The question I put
was along the lines of a hint as also the second thing I proposed in post #82. In any case, I can't see / understand the sort of contradiction you talk about and where is all this leading to.

In mathematical circles, if there is an error in a mathematical proof, they correctly specify where the error is located.

It was so memorable to me that even the great Newton taught in one of his books, solving the word problems for the students, or rather for their teachers. For example, I would like to calculate an ordinary definite integral, which would, of course, be given to me by a symbolic integration program, but then I would not learn anything.
 
  • #86
Periwinkle said:
In mathematical circles, if there is an error in a mathematical proof, they correctly specify where the error is located.

I see your point but on the other hand I cannot be inside everyone's mind to see / understand his / her way of thinking in every case, if it is not sufficiently explained / clear, at least for what my math knowledge allows me to understand. So, in order to be as helpful as I can, what can I tell is that I cannot make sense of what you write as a whole. Also, you go from one implicit way to another while you can prove what is asked directly - i.e. not in the way of involved and not involved, involved or not involved etc., in no more than ten lines, as I did for example - I don't claim any sort of superiority or anything; just an example, when I was given this problem at my nineteen in a mathematical contest.

Also, I see that you don't use my hints at all. This is perfect but they are along the lines of a direct way of proof for this problem and I think that they would be of help to you. In any case I won't try to surmise things that seem to lead somewhere; I ask for an explicit i.e. clear proof of what is asked.
 
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  • #87
Periwinkle said:
I.

My reasoning above, which I wrote yesterday before and hastened, is based on that
  1. for each ##X## person, we count the number of pairs of individuals for which this ##X## is involved
  2. for each pair of ##X, Y## persons, count the number of other persons involved in this pair.
That is, the number of existing involved relationships is counted in two ways.

(Why is it called "involved"? Because if I know both of the two people at the same time, or I don't know any of them, I will not say to either one that is unfavorable to the other. While in the other case, it can happen, and so I am involved in their relationship.)View attachment 245126
1. For each ##A_i## person, we count the number of ##(X, Y)## pairs of individuals for which this ##A_i## is involved and not involved

Mark participants in the conference with ##A_1, A_2, \dots A_n##. Person ##A_i## knows ##k_i## other persons, does not knows ##l_i##. Then it is not involved in ## \binom {k_i} 2 ## pairs because knows both of them, in case of ##\binom {l_i} 2 ## it is not involved, because does not know any of them. Involved in ##k_i \cdot l_i## pairs.

There are a total of ##\binom n 2 ## pairs of persons, for each pair, there are ##n-2## individuals who are either involved or not.

$$\binom n 2 (n-2) = \sum_{i=1}^n \left[\binom {k_i} 2 + \binom {l_i} k + k_i \cdot l_i \right] $$ $$\binom n 2 (n-2) = \sum_{i=1}^n \left[ \frac {k_i(k_i-1)} 2 + \frac {l_i(l_i-1)} 2 + k_i \cdot l_i \right]$$ $$\frac {n(n-1)} 2 (n-2) = \sum_{i=1}^n \frac {{k_i}^2-k_i + {l_i}^2-l_i + 2k_i\cdot l_i} 2 $$ $$\frac {n(n-1)(n-2)} 2 = \sum_{i=1}^n \frac {{k_i}^2+{l_i}^2-(n-1)+ 2k_i\cdot l_i} 2 $$ $$\frac {n(n-1)(n-2)} 2 = -\frac {n(n-1)} 2 + \sum_{i=1}^n \frac {{k_i}^2+{l_i}^2+ 2k_i\cdot l_i} 2 $$ But $$\frac {k_i+l_i} 2 \geq \sqrt{k_il_i}$$ so $$k_i^2+l_i^2\geq2k_il_i$$ Consequently
$$\frac {n(n-1)(n-2)} 2 +\frac {n(n-1)} 2 \geq \sum_{i=1}^n {2k_i\cdot l_i} $$ $$\frac {n(n-1)(n-1)} 2 \geq \sum_{i=1}^n {2k_i\cdot l_i} $$ $$\frac {n(n-1)} 2 \frac {n-1} 2 \geq \sum_{i=1}^n {k_i\cdot l_i} $$ 2. For each pair of ##(X, Y)## persons, count the number of other persons ##A_i## involved in this pair

For all ##n## natural numbers, $$ n- \left(\left[\frac n 2\right]-1 \right) \gt \frac {n-1} 2$$ Suppose the theorem is not true, so for each person pair, the number of people not involved in the pair is less than ##\left[ \frac n 2 \right]-1##. As a result of the above inequality, therefore, for each pair of persons, the number of persons involved is more than ##\frac {n-1} 2##. There are ##\binom n 2## pairs of people, so the total number of involved relationships is over $$\frac {n(n-1)} 2 \cdot \frac{n-1} 2$$ So we got a contradiction because the number of involved relationships should not be more than ##\frac {n(n-1)} 2 \cdot \frac{n-1} 2## and at the same time less than or equal to ##\frac {n(n-1)} 2 \cdot \frac{n-1} 2##.

II.

To prove the inequality of the floor-integer value function

If ##n=2k##, then $$n-\left( \left[ \frac n 2 \right] -1 \right) = 2k-k+1 = k+1 > k-\frac 1 2 = \frac {n-1} 2$$
If ##n=2k+1##, then $$n-\left(\left[ \frac n 2 \right]-1 \right) = 2k+1-k+1 = k+2 > k = \frac {n-1} 2$$

I think this is basically correct, although it is difficult to read. This is partly due to the question itself and partly to the definition of "involved". I will present my version of the proof, which should be similar to the above, but what I think, easier to grasp.

Statement: (as I understood it)
There is a pair ##(r,s)## among the ##n## people ##S:=\{\,1,2,\ldots,n\,\}## of a conference, such that the number of persons which either know both of them or neither of them is at least ##\displaystyle{ \lfloor \dfrac{n-1}{2} \rfloor }\,.##

Preliminaries:
I will omit the notation ##A_1,\ldots,A_n## and call the persons by numbers for short.
The symbol ##\dot{\vee}## will denote either or (XOR) and ##|\, . \,|## will denote the number of elements in a set. Furthermore we are given a symmetric relation ##R\subseteq S\times S## which means ##(r,s)\in R ## iff ##r## knows ##s##. The number of relevant pairs due to symmetry is ##\binom{n}{2}##. Therefore if we write ##S^2## we actually mean ##S^2/\sim ##, i.e. all pairs modulo symmetry. We do not count any pairs ##(x,x)\in R\,.##

We define the complement of the set we are interested in as $$I(x) :=\{\,(r,s)\in S^2\,|\,(x,r)\in R \,\dot{\vee}\, (x,s)\in R\,\}\; , \;l(x):=|I(x)|$$
i.e. the number of pairs relative to a person ##x##, where ##x## knows exactly one of them. We will further need the set of all triplets
$$T:=\{\,(x;r,s)\,|\,x\in S\, , \,(r,s)\in I(x)\,\}$$
of persons and associated pairs, such that the person knows exactly one of the pair.

Proof: For a person ##x## who knows exactly ##k(x)## persons, we have ##l(x)=k(x)\cdot (n-1-k(x))##. This function takes its theoretical maximum at ##k(x)=\dfrac{n-1}{2}## so that we have
$$
\max_{x\in S} l(x) \leq \dfrac{n-1}{2} \cdot \left( n-1-\dfrac{n-1}{2} \right) = \left( \dfrac{n-1}{2} \right)^2
$$
hence ##|T| \leq n\cdot \left( \dfrac{n-1}{2} \right)^2 = \dfrac{n-1}{2} \displaystyle{\binom{n}{2}}\,.##

Let us assume that for all pairs ##(r,s)\in S^2## we had ##|\{\,x\,|\,(x;r,s)\in T\,\}|> \dfrac{n-1}{2}\,.## Then the disjoint union
$$
\dot{\bigcup}_{(r,s)\in S^2} \{\,(x;r,s) \in T\,\}
$$
has more than ##\displaystyle{\binom{n}{2}}\cdot \dfrac{n-1}{2}## many elements. However, as it is a subset of ##T## which has less elements, our assumption was wrong. This means there is a pair ##(r,s)\in S^2## such that ##|\{\,x\in S\,|\,(x;r,s)\in T\,\}| \leq \dfrac{n-1}{2}##. For this pair ##(r,s)## we thus have
$$
|\{\,x\,|\,(x;r,s) \notin T\,\}| > (n-1) - \dfrac{n-1}{2} = \dfrac{n-1}{2} \geq \displaystyle{ \lfloor \dfrac{n-1}{2} \rfloor }
$$
which is what we wanted to prove.
 
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