I am not so good in math and I have an interview question to answer but I can`t understand this equation:
the interview question is:
write a function that, given an integer N(1 <= N <= 100) returns array containing N unique integer that sum up to 0.
I cant understand it to solve it please help what N(1 <= N <= 100) this mean.
P.S: I am not looking for ready solution, I want understand the concept so I can solve it my self.
Thanks.
Edit: its not a range from 1,100 as you`re suggesting, here is the question image
It means that both (1 <= N) and (N <= 100). So N is an integer from 1 to 100.
N(1 <= N <= 100)
simply means that you have a number N that is greater or equal to 1 and smaller or equal to 100.
In other words, it is an integer from 1 to 100 with both bounds inclusive.
It can also be written like that: N ∈ [1, 100] ∩ ℤ or N ∈ {x ∈ ℤ: 1 ≤ x ≤ 100}
Related
function A(n):
if n ≤ 10 then
return 1 fi;
x := 0;
for i = 1 to n do
x := x + 1 od;
return x * A(n/3) * A(n/6) * A(n/4)
My first idea was, that every call of A(n/c) is in O(log n) and since each has a for-loop from 1 to n it should be O(n log n). But since each call of A() also evokes 3 more it should also be somewhat exponential, right?
The calculation of x simply assigns n to it with n steps. So we can assume the loop is just a dummy n steps.
The rest of the function can be brought down to:
return n * (A(n/3) ** 3);
In every recursive step, A is divided by 3. This means we effectively get a sum of n + n/3 + n/9 + ... until n/3 reaches < 0.5.
The whole thing needs to be multiplied by 3, but this itself won't change anything complexity-wise. Now such a sum (E(i = 0, inf) of n/(k^i)) converges to n/k-1, which is O(n) given a constant k. Of course doing actual divisions by 6 or 4 won't change anything either.
So the complexity of your entire function is O(n).
Is it safe to replace a/(b*c) with a/b/c when using integer-division on positive integers a,b,c, or am I at risk losing information?
I did some random tests and couldn't find an example of a/(b*c) != a/b/c, so I'm pretty sure it's safe but not quite sure how to prove it.
Thank you.
Mathematics
As mathematical expressions, ⌊a/(bc)⌋ and ⌊⌊a/b⌋/c⌋ are equivalent whenever b is nonzero and c is a positive integer (and in particular for positive integers a, b, c). The standard reference for these sorts of things is the delightful book Concrete Mathematics: A Foundation for Computer Science by Graham, Knuth and Patashnik. In it, Chapter 3 is mostly on floors and ceilings, and this is proved on page 71 as a part of a far more general result:
In the 3.10 above, you can define x = a/b (mathematical, i.e. real division), and f(x) = x/c (exact division again), and plug those into the result on the left ⌊f(x)⌋ = ⌊f(⌊x⌋)⌋ (after verifying that the conditions on f hold here) to get ⌊a/(bc)⌋ on the LHS equal to ⌊⌊a/b⌋/c⌋ on the RHS.
If we don't want to rely on a reference in a book, we can prove ⌊a/(bc)⌋ = ⌊⌊a/b⌋/c⌋ directly using their methods. Note that with x = a/b (the real number), what we're trying to prove is that ⌊x/c⌋ = ⌊⌊x⌋/c⌋. So:
if x is an integer, then there is nothing to prove, as x = ⌊x⌋.
Otherwise, ⌊x⌋ < x, so ⌊x⌋/c < x/c which means that ⌊⌊x⌋/c⌋ ≤ ⌊x/c⌋. (We want to show it's equal.) Suppose, for the sake of contradiction, that ⌊⌊x⌋/c⌋ < ⌊x/c⌋ then there must be a number y such that ⌊x⌋ < y ≤ x and y/c = ⌊x/c⌋. (As we increase a number from ⌊x⌋ to x and consider division by c, somewhere we must hit the exact value ⌊x/c⌋.) But this means that y = c*⌊x/c⌋ is an integer between ⌊x⌋ and x, which is a contradiction!
This proves the result.
Programming
#include <stdio.h>
int main() {
unsigned int a = 142857;
unsigned int b = 65537;
unsigned int c = 65537;
printf("a/(b*c) = %d\n", a/(b*c));
printf("a/b/c = %d\n", a/b/c);
}
prints (with 32-bit integers),
a/(b*c) = 1
a/b/c = 0
(I used unsigned integers as overflow behaviour for them is well-defined, so the above output is guaranteed. With signed integers, overflow is undefined behaviour, so the program can in fact print (or do) anything, which only reinforces the point that the results can be different.)
But if you don't have overflow, then the values you get in your program are equal to their mathematical values (that is, a/(b*c) in your code is equal to the mathematical value ⌊a/(bc)⌋, and a/b/c in code is equal to the mathematical value ⌊⌊a/b⌋/c⌋), which we've proved are equal. So it is safe to replace a/(b*c) in code by a/b/c when b*c is small enough not to overflow.
While b*c could overflow (in C) for the original computation, a/b/c can't overflow, so we don't need to worry about overflow for the forward replacement a/(b*c) -> a/b/c. We would need to worry about it the other way around, though.
Let x = a/b/c. Then a/b == x*c + y for some y < c, and a == (x*c + y)*b + z for some z < b.
Thus, a == x*b*c + y*b + z. y*b + z is at most b*c-1, so x*b*c <= a <= (x+1)*b*c, and a/(b*c) == x.
Thus, a/b/c == a/(b*c), and replacing a/(b*c) by a/b/c is safe.
Nested floor division can be reordered as long as you keep track of your divisors and dividends.
#python3.x
x // m // n = x // (m * n)
#python2.x
x / m / n = x / (m * n)
Proof (sucks without LaTeX :( ) in python3.x:
Let k = x // m
then k - 1 < x / m <= k
and (k - 1) / n < x / (m * n) <= k / n
In addition, (x // m) // n = k // n
and because x // m <= x / m and (x // m) // n <= (x / m) // n
k // n <= x // (m * n)
Now, if k // n < x // (m * n)
then k / n < x / (m * n)
and this contradicts the above statement that x / (m * n) <= k / n
so if k // n <= x // (m * n) and k // n !< x // (m * n)
then k // n = x // (m * n)
and (x // m) // n = x // (m * n)
https://en.wikipedia.org/wiki/Floor_and_ceiling_functions#Nested_divisions
I cant seem to find any kind of answer to this, but if I have an equation like the square root of (X^2-4n) where 4n is a constant, how could I set x so the equation gives a whole number.
I know setting x to n+1 works, but I'm looking for an algorithm that would generate all solutions.
So, the problem is to find all pairs of integers (x, m) such that:
sqrt(x^2 - 4n) = m
We have:
x^2 - 4n = m^2
or
x^2 - mˆ2 = 4n
so
(x + m)(x - m) = 4n
Now, 2 divides 4n and so it must divide (x+m) or (x-m). But if it divides any of them it will divide the other too. Thus a := (x+m)/2 and b := (x-m)/2 are both integers. Therefore
a*b = n
So, it is just a matter of factoring n as a*b in all possible ways and recover x and m from the equations above:
x = a + b.
m = a - b.
Your solution x = n+1 corresponds to the trivial factorization n = n*1 where a=n and b=1.
UPDATE
Here is an algorithm that prints all pairs (x, m)
[Initialize] a := n.
[Check] if n % a = 0 then
b := n / a.
print(a + b), print(a - b)
[Decrement] a := a - 1.
[End?] if a * a > n go to Step 2.
I have floor(sqrt(floor(x))). Which is true:
The inner floor is redundant.
The outer floor is redundant.
Obviously the outer floor is not redundant, since for example, sqrt(2) is not an integer, and thus floor(sqrt(2))≠sqrt(2).
It is also easy to see that sqrt(floor(x))≠sqrt(x) for non-integer x. Since sqrt is a monotone function.
We need to find out whether or not floor(sqrt(floor(x)))==floor(sqrt(x)) for all rationals (or reals).
Let us prove that if sqrt(n)<m then sqrt(n+1)<m+1, for integers m,n. It is easy to see that
n<m^2 ⇒ n+1 < m^2+1 < m^2+2m+1 = (m+1)^2
Therefor by the fact that sqrt is montone we have that
sqrt(n) < m -> sqrt(n+1) < m+1 -> sqrt(n+eps)<m+1 for 0<=eps<1
Therefor floor(sqrt(n))=floor(sqrt(n+eps)) for all 0<eps<1 and integer n. Assume otherwise that floor(sqrt(n))=m and floor(sqrt(n+eps))=m+1, and you've got a case where sqrt(n)<m+1 however sqrt(n+eps)>=m+1.
So, assuming the outer floor is needed, the inner floor is redundant.
To put it otherwise it is always true that
floor(sqrt(n)) == floor(sqrt(floor(n)))
What about inner ceil?
It is easy to see that floor(sqrt(n)) ≠ floor(sqrt(ceil(n))). For example
floor(sqrt(0.001))=0, while floor(sqrt(1))=1
However you can prove in similar way that
ceil(sqrt(n)) == ceil(sqrt(ceil(n)))
The inner one is redundant, the outer one of course not.
The outer one is not redundant, because the square root of a number x only results in an integer if x is a square number.
The inner one is redundant, because the square root for any number in the interval [x,x+1[ (where x is an integer) always lies within the interval [floor(sqrt(x)),ceil(sqrt(x))[ and therefore you don't need to floor a number before taking the square root of it, if you are only interested the integer part of the result.
Intuitively I believe the inner one is redundant, but I can't prove it.
You're not allowed to vote me down unless you can provide a value of x that proves me wrong. 8-)
Edit: See v3's comment on this answer for proof - thanks, v3!
The inner floor is redundant
The inner floor is redundant. A proof by contradiction:
Assume the inner floor is not redundant. That would mean that:
floor(sqrt(x)) != floor(sqrt(x+d))
for some x and d where floor(x) = floor(x+d). Then we have three numbers to consider:
a = sqrt(x), b = floor(sqrt(x+d)), c = sqrt(x+d). b is an integer, and a < b < c.
That means that a^2 < b^2 < c^2, or x < b^2 < x+d. But if b is an integer,
then b^2 is an integer. Therefore floor(x) < b^2, and b^2 <= floor(x+d), and then floor(x) < floor(x+d). But we started by assuming floor(x) = floor(x+d). We've reached a contradiction, so our assumption is false, and the inner floor is redundant.
If x is an integer then the inner floor is redundant.
If x is not an integer then neither are redundant.
The outer floor is not redundant. Counterexample: x = 2.
floor(sqrt(floor(2))) = floor(sqrt(2)) = floor(1.41...)
Without the outer floor the result would be 1.41...
If the inner floor were not redundant, then we would expect that floor(sqrt(n)) != floor(sqrt(m)), where m = floor(n)
note that n - 1 < m <= n. m is always less than or equal to n
floor(sqrt(n)) != floor(sqrt(m)) requires that the values of sqrt(n) and sqrt(m) differ by at least 1.0
however, there are no values n for which the sqrt(n) differs by at least 1.0 from sqrt(n + 1), since for all values between 0 and 1 the sqrt of that value is < 1 by definition.
thus, for all values n, the floor(sqrt(n)) == floor(sqrt(n + 1)). This is in contradiction to the original assumption.
Thus the inner floor is redundant.
If n^2 <= x < (n+1)^2 where n is an integer, then
n <= sqrt(x) < n+1, so floor(sqrt(x)) = n;
n^2 <= floor(x) < (n+1)^2, so n <= sqrt(floor(x)) < n+1, so floor(sqrt(floor(x))) = n.
Therefore, floor(sqrt(floor(x))) = floor(sqrt(x)), which implies the inner floor is redundant.
Can someone explain mathematical induction to prove a recursive method? I am a freshmen computer science student and I have not yet taken Calculus (I have had up through Trig). I kind of understand it but I have trouble when asked to write out an induction proof for a recursive method.
Here is a explanation by example:
Let's say you have the following formula that you want to prove:
sum(i | i <- [1, n]) = n * (n + 1) / 2
This formula provides a closed form for the sum of all integers between 1 and n.
We will start by proving the formula for the simple base case of n = 1. In this case, both sides of the formula reduce to 1. This in turn means that the formula holds for n = 1.
Next, we will prove that if the formula holds for a value n, then it holds for the next value of n (or n + 1). In other words, if the following is true:
sum(i | i <- [1, n]) = n * (n + 1) / 2
Then the following is also true:
sum(i | i <- [1, n + 1]) = (n + 1) * (n + 2) / 2
To do so, let's start with the first side of the last formula:
s1 = sum(i | i <- [1, n + 1]) = sum(i | i <- [1, n]) + (n + 1)
That is, the sum of all integers between 1 and n + 1 is equal to the sum of integers between 1 and n, plus the last term n + 1.
Since we are basing this proof on the condition that the formula holds for n, we can write:
s1 = n * (n + 1) / 2 + (n + 1) = (n + 1) * (n + 2) / 2 = s2
As you can see, we have arrived at the second side of the formula we are trying to prove, which means that the formula does indeed hold.
This finishes the inductive proof, but what does it actually mean?
The formula is correct for n = 0.
If the formula is correct for n, then it is correct for n + 1.
From 1 and 2, we can say: if the formula is correct for n = 0, then it is correct for 0 + 1 = 1. Since we proved the case of n = 0, then the case of n = 1 is indeed correct.
We can repeat this above process again. The case of n = 1 is correct, then the case of n = 2 is correct. This reasoning can go ad infinitum; the formula is correct for all integer values of n >= 1.
induction != Calc!!!
I can get N guys drunk with 10*N beers.
Base Case: 1 guy
I can get one guy drunk with 10 beers
Inductive step, given p(n) prove p(n + 1)
I can get i guys drunk with 10 * i beers, if I add another guy, I can get him drunk with 10 more beers. Therefore, I can get i + 1 guys drunk with 10 * (i + 1) beers.
p(1) -> p(i + 1) -> p(i + 2) ... p(inf)
Discrete Math is easy!
First, you need a base case. Then you need an inductive step that holds true for some step n. In your inductive step, you will need an inductive hypothesis. That hypothesis is the assumption that you needed to have made. Finally, use that assumption to prove step n+1