More patterns for coding interviews ||

Unpopular patterns that show up often in coding interviews
if you have not read part1, you should start with it

4- Multisource BFS

This pattern is used when you need to traverse all points at a similar depth level at once and process them according to the problem statement or find the shortest distance starting from multiple points

It's very similar to ordinary BFS except you pop the whole queue each step instead of poping one node only


1162. As Far from Land as Possible

class Solution:
def maxDistance(self, grid: List[List[int]]) -> int:
n, level = len(grid), 0
queue = deque([(i, j) for i, j in product(range(n), range(n)) if grid[i][j]])
if len(queue) in (0,n*n): return -1
while queue:
for _ in range(len(queue)):
i,j = queue.popleft()
for x, y in [(1,0), (-1, 0), (0, 1), (0, -1)]:
xi, yj = x+i, y+j
if 0 <= xi < n and 0 <= yj < n and grid[xi][yj] == 0:
queue.append((xi, yj))
grid[xi][yj] = 1
level += 1
return level-1

542. 01 Matrix

class Solution:
def updateMatrix(self, matrix: List[List[int]]) -> List[List[int]]:
n, m, level, seen = len(matrix),len(matrix[0]), 0, set()
queue = deque([(i, j) for i, j in product(range(n), range(m)) if not matrix[i][j]])

while queue:
for _ in range(len(queue)):
i,j = queue.popleft()
if (i, j) not in seen:
seen.add((i, j))
matrix[i][j] = level
for x, y in [(1,0), (-1, 0), (0, 1), (0, -1)]:
xi, yj = x+i, y+j
if 0 <= xi < n and 0 <= yj < m: queue.append((xi, yj))
level += 1
return matrix

5- Running sum

This pattern solves problems where mathematical operations are repeated each step unnecessarily.
For example, 1+2+3 is (1+2)+3 so if you calculated 1+2 previously, you don’t need to repeat it, you can add 3 to the result instead. This reduces the complexity of each step from O(N) to O(1).

It works by keeping a running sum/count/multiplication…etc, either left to right (prefix), right to left (suffix), or both.


926. Flip String to Monotone Increasing(Amazon)

class Solution:
def minFlipsMonoIncr(self, s: str) -> int:
zeros_to_left, ones_to_right, minflips = [0]*(len(s)+1),[0]*(len(s)+1), float('inf')

for i, x in enumerate(s): zeros_to_left[i+1]=zeros_to_left[i]+(x=='0')
for i, x in enumerate(s[::-1]): ones_to_right[-i-2]=ones_to_right[-i-1]+(x=='1')
for i in range(len(s)+1): minflips = min(minflips, len(s)-zeros_to_left[i]-ones_to_right[i])

return minflips

238. Product of Array Except Self

class Solution:
def productExceptSelf(self, nums: List[int]) -> List[int]:
left_prod, right_prod = [1]*len(nums), [1]*len(nums)

for i in range(1,len(nums)): left_prod[i] = left_prod[i-1] * nums[i-1]
for i in range(len(nums)-2,-1,-1): right_prod[i] = right_prod[i+1]* nums[i+1]
for i in range(len(nums)): nums[i]=left_prod[i]*right_prod[i]

return nums

6- Range addition (Amazon)

This pattern solves the range addition problems in linear time instead of quadratic.
Range addition problems are problems where you add a number k to a subarray(range) for a number of updates n. for example, given array a[0,0,0,0] and updates [[2 ,[0,1]], [3 ,[1,2]]]. for the first update a =[2, 2, 0, 0], second update a = [2, 5, 0, 0]

The algorithm: for each update, k [start, end], add k to the start and subtract it from the end. then return the prefix sum for the array

There is mathematical proof of why This algorithm works you can check it here.


370. Range Addition

class Solution:
def RangeSum(self, n, updates):
ans = [0]*n
for update in updates:
if update[1]<n-1: ans[update[1]+1]-=update[2]
for i in range(1, n): ans[i]+=ans[i-1]
return ans

7- Matrix diagonal iteration

Diagonals and counter-diagonals in matrices have special properties which are their coordinates’ subtraction and addition, respectively, are constants.

if you consider the main diagonal [a11, a22, a33, … ] m-n is always zero
The diagonal that starts from a12 is [a12, a23, a34, ..] m-n is always -1
For counter-diagonals, m+n is a constant.

You can use this constant as a key in a hash table and process the diagonal as a flat array according to the problem statement


766. Toeplitz Matrix

class Solution:
def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool:
memo = {}
for i in range(len(matrix)):
for j in range(len(matrix[0])):
if i-j in memo and memo[i-j] != matrix[i][j]: return False
memo[i-j] = matrix[i][j]
return True

1329. Sort the Matrix Diagonally

class Solution:
def diagonalSort(self, mat: List[List[int]]) -> List[List[int]]:
m, n = len(mat[0]), len(mat)
table = {}
for i in range(n):
for j in range(m):
table[i-j] = table.get(i-j, []) + [mat[i][j]]

for k,v in table.items(): v.sort(reverse=True)

for i in range(n):
for j in range(m):
mat[i][j] = table[i-j].pop()

return mat

8- Monotonic stack

This pattern is used to find the next greater/smaller element and the previous greater/smaller element for each element in a given sequence in linear time

let’s assume you want to find the next greater element, you would pop all elements smaller than this element, change their next greater element to be the current one, then add the current one to the stack.

with small modifications, you can find the next smaller and previous greater or smaller elements


496. Next Greater Element I

class Solution:
def nextGreaterElement(self, nums1: List[int], nums2: List[int]) -> List[int]:
stack, ans = [], [-1] * len(nums2)
for i, num in enumerate(nums2):
while stack and nums2[stack[-1]] < num: ans[stack.pop()] = num
return [ans[nums2.index(num)] for num in nums1]

907. Sum of Subarray Minimums (Facebook)

class Solution:
def sumSubarrayMins(self, arr: List[int]) -> int:
stack, ans, arr = [], 0, [float('-inf'),*arr,float('-inf')]
for i, num in enumerate(arr):
while stack and arr[stack[-1]]>num:
j = stack.pop()
ans += arr[j] * (i-j) * (j-stack[-1])
return ans%(10**9 + 7)

That’s it for today. clap and subscribe for more patterns/

UPDATE: Part3 is now published




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