# Triangular Series

A triangular series is a series of numbers where each number could be the row of an equilateral triangle.

So 1, 2, 3, 4, 5 is a triangular series, because you could stack the numbers like this:

Their sum is 15, which makes 15 a triangular number .

A triangular series always starts with 1 and increases by 1 with each number.

Since the only thing that changes in triangular series is the value of the highest number, it’s helpful to give that a name. Let’s call it nn.

# n is 8
1, 2, 3, 4, 5, 6, 7, 8


Triangular series are nice because no matter how large nn is, it’s always easy to find the total sum of all the numbers.

Take the example above. Notice that if we add the first and last numbers together, and then add the second and second-to-last numbers together, they have the same sum! This happens with every pair of numbers until we reach the middle. If we add up all the pairs of numbers, we get:

1 + 8 = 9
2 + 7 = 9
3 + 6 = 9
4 + 5 = 9


This is true for every triangular series :

1. Pairs of numbers on each side will always add up to the same value .
2. That value will always be 1 more than the series’ nn .

This gives us a pattern. Each pair’s sum is n+1n+1, and there are \frac{n}{2}2n​ pairs. So our total sum is:(n + 1) * \frac{n}{2}(n+1)∗2n​

Or:

\frac{n^2 + n}{2}2n2+n​

Ok, but does this work with triangular series with an odd number of elements? Yes. Let’s say n = 5n=5. So if we calculate the sum by hand:

1+2+3+4+5=151+2+3+4+5=15

And if we use the formula, we get the same answer:

\frac{5^2 + 5}{2}=15252+5​=15

One more thing:

What if we know the total sum , but we don’t know the value of nn?

Let’s say we have:

1 + 2 + 3 + \ldots + (n - 2) + (n - 1) + n = 781+2+3+…+(n−2)+(n−1)+n=78

No problem. We just use our formula and set it equal to the sum!

\frac{n^2 + n}{2}=782n2+n​=78

Now, we can rearrange our equation to get a quadratic equation (remember those?)

n^2 + n = 156n2+n=156n^2 + n - 156 = 0n2+n−156=0

\frac{-b\pm\sqrt{b^2-4ac}}{2a}2a−b±b2−4ac​​

If you don’t really remember how to use it, that’s cool. You can just use an online calculator. We don’t judge.

Taking the positive solution, we get n = 12n=12.

So for a triangular series, remember—the total sum is:

\frac{n^2 + n}{2}2n2+n​

I have a list of n + 1n+1 numbers. Every number in the range 1…n1…n appears once except for one number that appears twice.

Write a function for finding the number that appears twice.

### Gotchas

We can do this with O(1)O(1) additional memory.

### Breakdown

To avoid using up extra memory space, lets use some math!

### Solution

First , we sum all numbers 1…n1…n. We can do this using the equation:

\frac{n^2 + n}{2}2n2+n​

because the numbers in 1…n1…n are a triangular series. ↴

Second , we sum all numbers in our input list, which should be the same as our other sum but with our repeat number added in twice. So the difference between these two sums is the repeated number!

def find_repeat(numbers_list):
if len(numbers_list) < 2:
raise ValueError('Finding duplicate requires at least two numbers')

n = len(numbers_list) - 1
sum_without_duplicate = (n * n + n) / 2

actual_sum = sum(numbers_list)

return actual_sum - sum_without_duplicate


### Complexity

O(n)O(n) time. We can sum all the numbers 1…n1…n in O(1)O(1) time using the fancy formula, but it still takes O(n)O(n) time to sum all the numbers in our input list.

O(1)O(1) additional space—the only additional space we use is for numbers to hold the sums with and without the repeated value.

### Bonus

If our list contains huge numbers or is really long, our sum might be so big it causes an integer overflow. ↴ What are some ways to protect against this?

Suppose we had a list ↴ of nn integers sorted in ascending order . How quickly could we check if a given integer is in the list?

### Solution

Because the list is sorted, we can use binary search ↴ to find the item in O(\lg{n})O(lgn) time and O(1)O(1) additional space.

Write a function for doing an in-place ↴ shuffle of a list.

The shuffle must be “uniform,” meaning each item in the original list must have the same probability of ending up in each spot in the final list.

Assume that you have a function get_random(floor, ceiling) for getting a random integer that is >= floor and <= ceiling.

### Gotchas

A common first idea is to walk through the list and swap each element with a random other element. Like so:

import random

def get_random(floor, ceiling):
return random.randrange(floor, ceiling + 1)

def naive_shuffle(the_list):
# For each index in the list
for first_index in xrange(0, len(the_list) - 1):
# Grab a random other index
second_index = get_random(0, len(the_list) - 1)
# And swap the values
if second_index != first_index:
the_list[first_index], the_list[second_index] = \
the_list[second_index], the_list[first_index]


However, this does not give a uniform random distribution.

Why? We could calculate the exact probabilities of two outcomes to show they aren’t the same. But the math gets a little messy. Instead, think of it this way:

Suppose our list had 33 elements: [a, b, c]. This means it’ll make 33 calls to get_random(0, 2). That’s 33 random choices, each with 33 possibilities. So our total number of possible sets of choices is 333=273∗3∗3=27. Each of these 2727 sets of choices is equally probable.

But how many possible outcomes do we have? If you paid attention in stats class you might know the answer is 3!3!, which is 66. Or you can just list them by hand and count:

a, b, c
a, c, b
b, a, c
b, c, a
c, b, a
c, a, b


But our function has 2727 equally-probable sets of choices. 2727 is not evenly divisible by 66. So some of our 66 possible outcomes will be achievable with more sets of choices than others.

We can do this in a single pass. O(n)O(n) time and O(1)O(1) space.

A common mistake is to have a mostly-uniform shuffle where an item is less likely to stay where it started than it is to end up in any given slot. Each item should have the same probability of ending up in each spot, including the spot where it starts.

### Breakdown

It helps to start by ignoring the in-place ↴ requirement, then adapt the approach to work in place.

Also, the name “shuffle” can be slightly misleading—the point is to arrive at a random ordering of the items from the original list. Don’t fixate too much on preconceived notions of how you would “shuffle” e.g. a deck of cards.

How might we do this by hand?

We can simply choose a random item to be the first item in the resulting list, then choose another random item (from the items remaining) to be the second item in the resulting list, etc.

Assuming these choices were in fact random, this would give us a uniform shuffle. To prove it rigorously, we can show any given item aa has the same probability (\frac{1}{n}n1​) of ending up in any given spot.

First, some stats review: to get the probability of an outcome, you need to multiply the probabilities of all the steps required for that outcome . Like so:

Outcome Steps Probability
item #1 is a a is picked first \frac{1}{n}n1​
item #2 is a a not picked first, a picked second \frac{(n-1)}{n} * \frac{1}{(n-1)} =n(n−1)​∗(n−1)1​= \frac{1}{n}n1​
item #3 is a a not picked first, a not picked second, a picked third \frac{(n-1)}{n} * \frac{(n-2)}{(n-1)} * \frac{1}{(n-2)} =n(n−1)​∗(n−1)(n−2)​∗(n−2)1​= \frac{1}{n}n1​
item #4 is a a not picked first, a not picked second, a not picked third, a picked fourth \frac{(n-1)}{n} * \frac{(n-2)}{(n-1)} * \frac{(n-3)}{(n-2)} * \frac{1}{(n-3)} =n(n−1)​∗(n−1)(n−2)​∗(n−2)(n−3)​∗(n−3)1​= \frac{1}{n}n1​

So, how do we implement this in code?

If we didn’t have the “in-place” requirement, we could allocate a new list, then one-by-one take a random item from the input list, remove it, put it in the first position in the new list, and keep going until the input list is empty (well, probably a copy of the input list—best not to destroy the input)

How can we adapt this to be in place?

What if we make our new “random” list simply be the front of our input list?

### Solution

We choose a random item to move to the first index, then we choose a random other item to move to the second index, etc. We “place” an item in an index by swapping it with the item currently at that index.

Crucially, once an item is placed at an index it can’t be moved. So for the first index, we choose from nn items, for the second index we choose from n-1n−1 items, etc.

import random

def get_random(floor, ceiling):
return random.randrange(floor, ceiling + 1)

def shuffle(the_list):
# If it's 1 or 0 items, just return
if len(the_list) <= 1:
return the_list

last_index_in_the_list = len(the_list) - 1

# Walk through from beginning to end
for index_we_are_choosing_for in xrange(0, len(the_list) - 1):

# Choose a random not-yet-placed item to place there
# (could also be the item currently in that spot)
# Must be an item AFTER the current item, because the stuff
# before has all already been placed
random_choice_index = get_random(index_we_are_choosing_for,
last_index_in_the_list)

# Place our random choice in the spot by swapping
if random_choice_index != index_we_are_choosing_for:
the_list[index_we_are_choosing_for], the_list[random_choice_index] = \
the_list[random_choice_index], the_list[index_we_are_choosing_for]


This is a semi-famous algorithm known as the Fisher-Yates shuffle (sometimes called the Knuth shuffle).

### Complexity

O(n)O(n) time and O(1)O(1) space.

### What We Learned

Don’t worry, most interviewers won’t expect a candidate to know the Fisher-Yates shuffle algorithm. Instead, they’ll be looking for the problem-solving skills to derive the algorithm, perhaps with a couple hints along the way.

They may also be looking for an understanding of why the naive solution is non-uniform (some outcomes are more likely than others). If you had trouble with that part, try walking through it again.

You have a function rand7() that generates a random integer from 1 to 7. Use it to write a function rand5() that generates a random integer from 1 to 5.

rand7() returns each integer with equal probability. rand5() must also return each integer with equal probability.

### Gotchas

Your first thought might be to simply take the result of rand7() and take a modulus:

def rand5():
return rand7() % 5 + 1


However, this won’t give an equal probability for each possible result . We can write out each possible result from rand7() (each of which is equally probable, per the problem statement) and see that some results for rand5() are more likely because they are caused by more results from rand7():

rand7() rand5()
1 2
2 3
3 4
4 5
5 1
6 2
7 3

So we see that there are two ways to get 2 and 3, but only one way to get 1, 4, or 5. This makes 2 and 3 twice as likely as the others.

What about calling rand7() five times, summing up the result, and then taking the modulus?

This is really close to uniform, but not quite. Since we’re calling rand7() five times, there are 7^5 = 16,80775=16,807 possible results. That’s not divisible by five, so some outcomes must be more likely than others. (If you’re curious, 1 is the result 3,357 times; 2 and 5 are the result 3,360 times each; and 3 and 4 are the result 3,365 times.)

In fact, no matter how many times we run rand7(), we’ll never get a number of outcomes that’s divisible by five. ↴

The answer takes worst-case infinite time. However, we can get away with worst-case O(1)O(1) space. Does your answer have a non-constant space cost? If you’re using recursion (and your language doesn’t have tail-call optimization ↴ ), you’re potentially incurring a worst-case infinite space cost in the call stack. ↴ But replacing your recursion with a loop avoids this.

### Breakdown

rand5() must return each integer with equal probability, but we don’t need to make any guarantees about its runtime…

In fact, the solution has a small possibility of never returning…

### Solution

We simply “re-roll” whenever we get a number greater than 5.

def rand5():
result = 7  # arbitrarily large
while result > 5:
result = rand7()
return result


So each integer 1,2,3,4, or 5 has a probability \frac{1}{7}71​ of appearing at each roll.

### Complexity

Worst-case O(\infty)O(∞) time (we might keep re-rolling forever) and O(1)O(1) space.

Note that if we weren’t worried about the potential space cost (nor the potential stack overflow ↴ ) of recursion, we could use an arguably-more-readable recursive approach with O(\infty)O(∞) space cost:

def rand5():
result = rand7()
return result if result <= 5 else rand5()


### Bonus

This kind of math is generally outside the scope of a coding interview, but: if you know a bit of number theory you can prove that there exists no solution which is guaranteed to terminate. Hint: it follows from the fundamental theorem of arithmetic. ↴

### What We Learned

Making sure each possible result has the same probability is a big part of what makes this one tricky.

If you’re ever struggling with the math to figure something like that out, don’t be afraid to just count . As in, write out all the possible results from rand7(), and label each one with its final outcome for rand5(). Then count up how many ways there are to make each final outcome. If the counts aren’t the same, the function isn’t uniformly random.

You have a function rand5() that generates a random integer from 1 to 5. Use it to write a function rand7() that generates a random integer from 1 to 7.

rand5() returns each integer with equal probability. rand7() must also return each integer with equal probability.

### Gotchas

Simply running rand5() twice, adding the results, and taking a modulus won’t give us an equal probability for each possible result.

Not convinced? Count the number of ways to get each possible result from 1…71…7.

Your function will have worst-case infinite runtime, because sometimes it will need to “try again.”

However, at each “try” you only need to make two calls to rand5(). If you’re making 3 calls, you can do better.

We can get away with worst-case O(1)O(1) space. Does your answer have a non-constant space cost? If you’re using recursion (and your language doesn’t have tail-call optimization ↴ ), you’re potentially incurring a worst-case infinite space cost in the call stack. ↴

### Breakdown

Because we need a random integer between 1 and 7, we need at least 7 possible outcomes of our calls to rand5(). One call to rand5() only has 5 possible outcomes. So we must call rand5() at least twice.

Can we get away with calling rand5() exactly twice?

Our first thought might be to simply add two calls to rand5(), then take a modulus to convert it to an integer in the range 1…71…7:

def rand7_mod():
return (rand5() + rand5()) % 7 + 1


However, this won’t give us an equal probability of getting each integer in the range 1…71…7. Can you see why?

There are at least two ways to show that different results of rand7_mod() have different probabilities of occurring:

1. Count the number of outcomes of our two rand5() calls which give each possible result of rand7_mod()
2. Notice something about the total number of outcomes of two calls to rand5()

If we count the number of ways to get each result of rand7_mod():

result of rand7_mod() # pairs of rand5() results that give that result
1 4
2 3
3 3
4 3
5 3
6 4
7 5

So we see that, for example, there are five outcomes that give us 7 but only three outcomes that give us 5. We’re almost twice as likely to get a 7 as we are to get a 5.

But even without counting the number of ways to get each possible result, we could have noticed something about the total number of outcomes of two calls to rand5() , which is 25 (5*5). If each of our 7 results of rand7_mod() were equally probable, we’d need to have the same number of outcomes for each of the 7 integers in the range 1…71…7. That means our total number of outcomes would have to be divisible by 7 , and 25 is not.

Okay, so rand7_mod() won’t work. How do we get equal probabilities for each integer from 1 to 7?

Is there some number of calls we can make to rand5() to get a number of outcomes that is divisible by 7?

When we roll our die nn times, we get 5^n5n possible outcomes. Is there an integer nn that will give us a 5^n5n that’s evenly divisible by 7?

No, there isn’t.

That might not be obvious to you unless you’ve studied some number theory. It turns out every integer can be expressed as a product of prime numbers (its prime factorization ↴ ). It also turns out that every integer has only one prime factorization.

Since 5 is already prime, any number that can be expressed as 5^n5n (where nn is a positive integer) will have a prime factorization that is all 5s. For example, here are the prime factorizations for 5^2, 5^3, 5^452,53,54:

5^2 = 25 = 5 * 552=25=5∗55^3 = 125 = 5 * 5 * 553=125=5∗5∗55^4 = 625 = 5 * 5 * 5 * 554=625=5∗5∗5∗5

7 is also prime, so if any power of 5 were divisible by 7, 7 would be in its prime factorization. But 7 can’t be in its prime factorization because its prime factorization is all 5s (and it has only one prime factorization). So no power of 5 is divisible by 7. BAM MATHPROOF.

So no matter how many times we run rand5() we won’t get a number of outcomes that’s evenly divisible by 7. What do we dooooo!?!?

Let’s ignore for a second the fact that 25 isn’t evenly divisible by 7. We can think of our 25 possible outcomes from 2 calls to rand5 as a set of 25 “slots” in a list:

results = [
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
]


Which we could then try to evenly distribute our 7 integers across:

results = [
1, 2, 3, 4, 5,
6, 7, 1, 2, 3,
4, 5, 6, 7, 1,
2, 3, 4, 5, 6,
7, 1, 2, 3, 4,
]


It almost works . We could randomly pick an integer from the list, and the chances of getting any integer in 1…71…7 are pretty evenly distributed. Only problem is that extra 1, 2, 3, 4 in the last row.

Any way we can sidestep this issue?

What if we just “throw out” those extraneous results in the last row?

21 is divisible by 7. So if we just “throw out” our last 4 possible outcomes, we have a number of outcomes that are evenly divisible by 7.

But what should we do if we get one of those 4 “throwaway” outcomes?

We can just try the whole process again!

Okay, this’ll work. But how do we translate our two calls to rand5() into the right result from our list?

What if we made it a 2-dimensional list?

results = [
[1, 2, 3, 4, 5],
[6, 7, 1, 2, 3],
[4, 5, 6, 7, 1],
[2, 3, 4, 5, 6],
[7, 1, 2, 3, 4],
]


Then we can simply treat our first roll as the row and our second roll as the column. We have an equal chance of choosing any column and any row, and there are never two ways to choose the same cell!

def rand7_table():
results = [
[1, 2, 3, 4, 5],
[6, 7, 1, 2, 3],
[4, 5, 6, 7, 1],
[2, 3, 4, 5, 6],
[7, 0, 0, 0, 0],
]

# Do our die rolls
row = rand5() - 1
column = rand5() - 1

# Case: we hit an extraneous outcome
# so we need to re-roll
if row == 4 and column > 0:
return rand7_table()

# Our outcome was fine. return it!
return results[row][column]


This’ll work. But we can clean things up a bit.

By using recursion we’re incurring a space cost in the call stack, and risking stack overflow. ↴ This is especially scary because our function could keep rerolling indefinitely (though it’s unlikely).

How can we rewrite this iteratively?

Just wrap it in a while loop:

def rand7_table():
results = [
[1, 2, 3, 4, 5],
[6, 7, 1, 2, 3],
[4, 5, 6, 7, 1],
[2, 3, 4, 5, 6],
[7, 0, 0, 0, 0],
]

while True:
# Do our die rolls
row = rand5() - 1
column = rand5() - 1

# Case: we hit an extraneous outcome
# so we need to re-roll
if row == 4 and column > 0:
continue

# Our outcome was fine. return it!
return results[row][column]


One more thing: we don’t have to put our whole 2-d results list in memory. Can you replace it with some arithmetic?

We could start by coming up with a way to translate each possible outcome (of our two rand5() calls) into a different integer in the range 1…251…25. Then we simply mod the result by 7 (or throw it out and try again, if it’s one of the last 4 “extraneous” outcomes).

How can we use math to turn our two calls to rand5() into a unique integer in the range 1…251…25?

What did we do when we went from a 1-dimensional list to a 2-dimensional one above? We cut our set of outcomes into sequential slices of 5.

How can we use math to make our first roll select which slice of 5 and our second roll select which item within that slice?

We could take something like:

outcome_number = roll1 * 5 + roll2


But since each roll gives us an integer in the range 1…51…5 our lowest possible outcome is two 1s, which gives us 5 + 1 = 65+1=6, and our highest possible outcome is two 5s, which gives us 25 + 5 = 3025+5=30. So we need to do some adjusting to ensure our outcome numbers are in the range 1…251…25:

outcome_number = ((roll1-1) * 5 + (roll2-1)) + 1


(If you’re a math-minded person, you might notice that we’re essentially treating each result of rand5() as a digit in a two-digit base-5 integer. The first roll is the fives digit, and the second roll is the ones digit.)

Can you adapt our function to use this math-based approach instead of the results list?

### Solution

Because rand5() has only 5 possible outcomes, and we need 7 possible results for rand7(), we need to call rand5() at least twice.

When we call rand5() twice, we have 5*5=255∗5=25 possible outcomes. If each of our 7 possible results has an equal chance of occurring, we’ll need each outcome to occur in our set of possible outcomes the same number of times . So our total number of possible outcomes must be divisible by 7.

25 isn’t evenly divisible by 7, but 21 is. So when we get one of the last 4 possible outcomes, we throw it out and roll again.

So we roll twice and translate the result into a unique outcome_number in the range 1…251…25. If the outcome_number is greater than 21, we throw it out and re-roll. If not, we mod by 7 (and add 1).

def rand7():
while True:
# Do our die rolls
roll1 = rand5()
roll2 = rand5()
outcome_number = (roll1-1) * 5 + (roll2-1) + 1

# If we hit an extraneous
# outcome we just re-roll
if outcome_number > 21:
continue

# Our outcome was fine. return it!
return outcome_number % 7 + 1


### Complexity

Worst-case O(\infty)O(∞) time (we might keep re-rolling forever) and O(1)O(1) space.

### What We Learned

As with the previous question about writing a rand5() function, the requirement to “return each integer with equal probability” is a real sticking point.

Lots of candidates come up with clever O(1)O(1)-time solutions that they are so sure about. But their solutions aren’t actually uniform (in other words, they’re not truly random ).

In fact, it’s impossible to have true randomness and non-infinite worst-case runtime.

If you don’t understand why, go back over our proof using “prime factorizations,” a little ways down in the breakdown section.

A building has 100 floors. One of the floors is the highest floor an egg can be dropped from without breaking.

If an egg is dropped from above that floor, it will break. If it is dropped from that floor or below, it will be completely undamaged and you can drop the egg again.

Given two eggs, find the highest floor an egg can be dropped from without breaking, with as few drops as possible.

### Gotchas

We can do better than a binary ↴ approach, which would have a worst case of 50 drops.

We can even do better than 19 drops!

We can always find the highest floor an egg can be dropped from with a worst case of 14 total drops.

### Breakdown

What if we only had one egg? How could we find the correct floor?

Because we can’t use the egg again if it breaks, we’d have to play it safe and drop the egg from every floor, starting at the bottom and working our way up. In the worst case, the egg won’t break until the top floor, so we’d drop the egg 100 times.

What does having two eggs allow us to do differently?

Since we have two eggs, we can skip multiple floors at a time until the first egg breaks, keeping track of which floors we dropped it from. Once that egg breaks we can use the second egg to try every floor, starting on the last floor where the egg didn’t break and ending on the floor below the one where it did break.

How should we choose how many floors to skip with the first egg?

What about trying a binary ↴ approach? We could drop the first egg halfway up the building at the 50th floor. If the egg doesn’t break, we can try the 75th floor next. We keep going like this, dividing the problem in half at each step. As soon as the first egg breaks, we can start using our second egg on our (now-hopefully narrow) range of possible floors.

If we do that, what’s the worst case number of total drops?

The worst case is that the highest floor an egg won’t break from is floor 48 or 49. We’d drop the first egg from the 50th floor, and then we’d have to drop the second egg from every floor from 1 to 49, for a total of 50 drops. (Even if the highest floor an egg won’t break from is floor 48, we still won’t know if it will break from floor 49 until we try.)

Can we do better than this binary approach?

50 is probably too many floors to skip for the first drop. In the worst case, if the first egg breaks after a small number of drops, the second egg will break after a large number of drops. And if we went the other way and skipped 1 floor every time, we’d have the opposite problem! What would the worst case floor be then?

The worst case would be floor 98 or 99—the first egg would drop a large number of times (at every floor from 2-100 skipping one floor each time) and the last egg would drop a small number of times (only on floor 99), for a total of 51 drops.

Can we balance this out? Is there some number between 50 and 1—the number of floors we’ll skip with each drop of the first egg—where the first and second eggs would drop close to the same number of times in the worst case?

Yes, we could skip 10 floors each time. The worst case would again be floor 98 or 99, but we’d only drop the first egg 10 times and the second egg 9 times, for a total of 19 drops!

Is that the best we can do?

Let’s look at what happens with this strategy each time the first egg doesn’t break . How does the worst case total number of drops change?

The worst case total number of drops increases by one each time the first egg doesn’t break

For example, if the egg breaks on our first drop from the 10th floor, we may have to drop the second egg at each floor between 1 and 9 for a worst case of 10 total drops. But if the egg breaks when we skip to the 20th floor we will have a worst case of 11 total drops (once for the 10th floor, once for the 20th, and all of the floors between 11 and 19)!

How can we keep the worst case number of drops from increasing each time the first egg doesn’t break?

Since the maximum number of drops increases by one each time we skip the same amount of floors , we could skip one fewer floor each time we drop the first egg!

But how do we choose how many floors to skip the first time?

Well, we know two things that can help us:

1. We want to skip as few floors as possible the first time we drop an egg, so if our first egg breaks right away we don’t have a lot of floors to drop our second egg from.
2. We always want to be able to reduce the number of floors we’re skipping by one . We don’t want to get towards the top and not be able to skip any floors any more.

Now that we know this, can we figure out the ideal number of floors to skip the first time?

To be able to decrease the number of floors we skip by one every time we move up, and to minimize the number of floors we skip the first time, we want to end up skipping just one floor at the very top . Can we model this with an equation?

Let’s say nn is the number of floors we’ll skip the first time, and we know 1 is the number of floors we’ll skip last. Our equation will be:

n + (n-1) + (n-2) + \ldots + 1 = 100n+(n−1)+(n−2)+…+1=100

How can we solve for nn? Notice that we’re summing every number from 1 to nn.

The left side is a triangular series. ↴ Knowing this, can we simplify and solve the equation?

We know the left side reduces to:

\frac{n^2 + n}{2}2n2+n​

so we can plug that in:

\frac{n^2 + n}{2}=1002n2+n​=100

and we can rearrange to get a quadratic equation:

n^2 + n - 200 = 0n2+n−200=0

which gives us 13.65113.651.

We can’t skip a fraction of a floor, so how many floors should we skip the first time? And what’s our final worst case total number of drops ?

### Solution

We’ll use the first egg to get a range of possible floors that contain the highest floor an egg can be dropped from without breaking. To do this, we’ll drop it from increasingly higher floors until it breaks, skipping some number of floors each time.

When the first egg breaks, we’ll use the second egg to find the exact highest floor an egg can be dropped from. We only have to drop the second egg starting from the last floor where the first egg didn’t break, up to the floor where the first egg did break. But we have to drop the second egg one floor at a time.

With the first egg, if we skip the same number of floors every time , the worst case number of drops will increase by one every time the first egg doesn’t break. To counter this and keep our worst case drops constant , we decrease the number of floors we skip by one each time we drop the first egg .

When we’re choosing how many floors to skip the first time we drop the first egg, we know:

1. We want to skip as few floors as possible, so if the first egg breaks right away we don’t have a lot of floors to drop our second egg from.
2. We always want to be able to reduce the number of floors we’re skipping. We don’t want to get towards the top and not be able to skip floors any more.

Knowing this, we set up the following equation where nn is the number of floors we skip the first time:

n + (n-1) + (n-2) + \ldots + 1 = 100n+(n−1)+(n−2)+…+1=100

This triangular series ↴ reduces to n * (n+1) / 2 = 100n∗(n+1)/2=100 which solves to give n = 13.651n=13.651. We round up to 14 to be safe. So our first drop will be from the 14th floor, our second will be 13 floors higher on the 27th floor and so on until the first egg breaks. Once it breaks, we’ll use the second egg to try every floor starting with the last floor where the first egg didn’t break. At worst, we’ll drop both eggs a combined total of 14 times.

For example:

Highest floor an egg won't break from
13

Floors we drop first egg from
14

Floors we drop second egg from
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13

Total number of drops
14

Highest floor an egg won't break from
98

Floors we drop first egg from
14, 27, 39, 50, 60, 69, 77, 84, 90, 95, 99

Floors we drop second egg from
96, 97, 98

Total number of drops
14


### What We Learned

This is one of our most contentious questions. Some people say, “Ugh, this is useless as an interview question,” while others say, “We ask this at my company, it works great.”

The bottom line is some companies do ask questions like this, so it’s worth being prepared. There are a bunch of these not-exactly-programming interview questions that lean on math and logic. There are some famous ones about shuffling cards and rolling dice. If math isn’t your strong suit, don’t fret. It only takes a few practice problems to get the hang of these.