Hi I was looking up solutions to a leetcode problem, I know how to solve the problem but someone else submitted this solution and I don't understand how this works.
The question is how many number of stacks can you form with n number of coins where k-th row has k number of coins. https://leetcode.com/explore/challenge/card/july-leetcoding-challenge/544/week-1-july-1st-july-7th/3377/
Returning the above formula works, can anyone explain it to me?
The sum of the first N natural numbers (1 + 2 + 3 + ... + N) is known to be equal to N(N+1)2
The game says that if you have 6 coins you have to stack them in this way:
x (1)
x x (2)
x x x (3)
and 6 is equal to 1 + 2 + 3.
If you are given K coins and you know that N(N+1)/2 = K then you know that you can have N rows. Now the question is, given K, how can you find N?
Let's do the math:
N(N+1)/2 = K
N^2 + N = 2*K
N^2 + N -2*K = 0
N = (-1 + sqrt(1 + 8K))/2
N = -1/2 + sqrt(1/4 + 2K)
N = sqrt(2*K + 0.25) - 0.5
Related
For n binary digits with base x, the maximum value will be:
x^(n-1) + x^(n-2) + ... + x^1 + x^0
By using geometric progression,
r=1/x
Using formula for sum of n finite numbers i get:
(x^n - 1) / (x - 1).
But my answer should have been x^n - 1.(formula: M= x^n - 1)
You are correct that the sum of the geometric series x0 + x1 + x2 + ... + xn-1 is indeed (xn - 1) / (x - 1). For example, if we pick x = 10 (base 10) and n = 3 (a three-digit base 10 number), we get back
1 + 10 + 100 = (1000 - 1) / 9 = 999 / 9 = 111.
However, the largest three-digit number is 999. And by looking at the above sum, you might get a sense of why we're off by a factor of 9. When writing out numbers in base 10, we'd maximize our number by having each digit be 9, not 1. And more generally, in base x, we'd maximize our value by having each digit be x - 1. That means that the maximum value is
(x - 1)(x0 + x1 + x2 + ... + xn-1) = (x - 1)(xn - 1)/(x - 1) = xn - 1.
Here's another, easier way to see this. What is the smallest number you can make with n+1 digits? That would be xn. Since that's the smallest (n+1)-digit number, the largest n-digit number must be that minus one, giving xn - 1 without needing to discuss geometric series.
I want to calculate the sum of the series as below
Lim
X->1 (2/3 - x/3 -(x^2)/3 +(x^3)*2/3 -..). I am not sure whether we have a formula for finding the sum of this kind of series. Tried a lot but couldn't find any. Any help is appreciated.
This seems to be more maths than computing.
It factorises as (1 + x^3 + x^6 + ...)(2 - x - x^2)/3
If x = 1-d (where d is small), then to first order in d, the (2 - x - x^2) term becomes (2 - (1-d) - (1-2d)) = 3d
And the (1 + x^3 + x^6 + ...) term is a geometric progression, with sum 1/(1-x^3), or here 1/(1-(1-d)^3), and the denominator to first order in d is (1 - (1-3d)) = 3d
Hence the whole thing is (1/3d) (3d) / 3 = 1/3
But we can also verify computationally with a value close to 1 (Python code here):
x = 0.999999
s = 0
f = (2 - x - x*x) / 3.
x3 = x ** 3
s_prev = None
while s != s_prev:
s_prev = s
s += f
f *= x3
print(s)
gives:
0.33333355556918565
Given a pyramid like:
0
1 2
3 4 5
6 7 8 9
...
and given the index of the pyramid i where i represents the ith number of the pyramid, is there a way to find the index of the row to which the ith element belongs? (e.g. if i = 6,7,8,9, it is in the 3rd row, starting from row 0)
There's a connection between the row numbers and the triangular numbers. The nth triangular number, denoted Tn, is given by Tn = n(n-1)/2. The first couple triangular numbers are 0, 1, 3, 6, 10, 15, etc., and if you'll notice, the starts of each row are given by the nth triangular number (the fact that they come from this triangle is where this name comes from.)
So really, the goal here is to determine the largest n such that Tn ≤ i. Without doing any clever math, you could solve this in time O(√n) by just computing T0, T1, T2, etc. until you find something bigger than i. Even better, you could binary search for it in time O(log n) by computing T1, T2, T4, T8, etc. until you overshoot, then binary searching on the range you found.
Alternatively, we could try to solve for this directly. Suppose we want to find the choice of n such that
n(n + 1) / 2 = i
Expanding, we get
n2 / 2 + n / 2 = i.
Equivalently,
n2 / 2 + n / 2 - i = 0,
or, more easily:
n2 + n - 2i = 0.
Now we use the quadratic formula:
n = (-1 ± √(1 + 8i)) / 2
The negative root we can ignore, so the value of n we want is
n = (-1 + √(1 + 8i)) / 2.
This number won't necessarily be an integer, so to find the row you want, we just round down:
row = ⌊(-1 + √(1 + 8i)) / 2⌋.
In code:
int row = int((-1 + sqrt(1 + 8 * i)) / 2);
Let's confirm that this works by testing it out a bit. Where does 9 go? Well, we have
(-1 + √(1 + 72)) / 2 = (-1 + √73) / 2 = 3.77
Rounding down, we see it goes in row 3 - which is correct!
Trying another one, where does 55 go? Well,
(-1 + √(1 + 440)) / 2 = (√441 - 1) / 2 = 10
So it should go in row 10. The tenth triangular number is T10 = 55, so in fact, 55 starts off that row. Looks like it works!
I get row = math.floor (√(2i + 0.25) - 0.5) where i is your number
Essentially the same as the guy above but I reduced n2 + n to (n + 0.5)2 - 0.25
I think ith element belongs nth row where n is number of n(n+1)/2 <= i < (n+1)(n+2)/2
For example, if i = 6, then n = 3 because n(n+1)/2 <= 6
and if i = 8, then n = 3 because n(n+1)/2 <= 8
I am having trouble understanding the concept of recurrences. Given you have T(n) = 2T(n/2) +1 how do you calculate the complexity of this relationship? I know in mergesort, the relationship is T(n) = 2T(n/2) + cn and you can see that you have a tree with depth log2^n and cn work at each level. But I am unsure how to proceed given a generic function. Any tutorials available that can clearly explain this?
The solution to your recurrence is T(n) ∈ Θ(n).
Let's expand the formula:
T(n) = 2*T(n/2) + 1. (Given)
T(n/2) = 2*T(n/4) + 1. (Replace n with n/2)
T(n/4) = 2*T(n/8) + 1. (Replace n with n/4)
T(n) = 2*(2*T(n/4) + 1) + 1 = 4*T(n/4) + 2 + 1. (Substitute)
T(n) = 2*(2*(2*T(n/8) + 1) + 1) + 1 = 8*T(n/8) + 4 + 2 + 1. (Substitute)
And do some observations and analysis:
We can see a pattern emerge: T(n) = 2k * T(n/2k) + (2k − 1).
Now, let k = log2 n. Then n = 2k.
Substituting, we get: T(n) = n * T(n/n) + (n − 1) = n * T(1) + n − 1.
For at least one n, we need to give T(n) a concrete value. So we suppose T(1) = 1.
Therefore, T(n) = n * 1 + n − 1 = 2*n − 1, which is in Θ(n).
Resources:
https://www.cs.duke.edu/courses/spring05/cps100/notes/slides07-4up.pdf
http://www.cs.duke.edu/~ola/ap/recurrence.html
However, for routine work, the normal way to solve these recurrences is to use the Master theorem.
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