# Posts tagged as “prefix”

You are given a 0-indexed integer array nums of length n.

The average difference of the index i is the absolute difference between the average of the first i + 1 elements of nums and the average of the last n - i - 1 elements. Both averages should be rounded down to the nearest integer.

Return the index with the minimum average difference. If there are multiple such indices, return the smallest one.

Note:

• The absolute difference of two numbers is the absolute value of their difference.
• The average of n elements is the sum of the n elements divided (integer division) by n.
• The average of 0 elements is considered to be 0.

Example 1:

Input: nums = [2,5,3,9,5,3]
Output: 3
Explanation:
- The average difference of index 0 is: |2 / 1 - (5 + 3 + 9 + 5 + 3) / 5| = |2 / 1 - 25 / 5| = |2 - 5| = 3.
- The average difference of index 1 is: |(2 + 5) / 2 - (3 + 9 + 5 + 3) / 4| = |7 / 2 - 20 / 4| = |3 - 5| = 2.
- The average difference of index 2 is: |(2 + 5 + 3) / 3 - (9 + 5 + 3) / 3| = |10 / 3 - 17 / 3| = |3 - 5| = 2.
- The average difference of index 3 is: |(2 + 5 + 3 + 9) / 4 - (5 + 3) / 2| = |19 / 4 - 8 / 2| = |4 - 4| = 0.
- The average difference of index 4 is: |(2 + 5 + 3 + 9 + 5) / 5 - 3 / 1| = |24 / 5 - 3 / 1| = |4 - 3| = 1.
- The average difference of index 5 is: |(2 + 5 + 3 + 9 + 5 + 3) / 6 - 0| = |27 / 6 - 0| = |4 - 0| = 4.
The average difference of index 3 is the minimum average difference so return 3.


Example 2:

Input: nums = [0]
Output: 0
Explanation:
The only index is 0 so return 0.
The average difference of index 0 is: |0 / 1 - 0| = |0 - 0| = 0.


Constraints:

• 1 <= nums.length <= 105
• 0 <= nums[i] <= 105

Solution: Prefix / Suffix Sum

Time complexity: O(n)
Space complexity: O(1)

## C++

wonderful string is a string where at most one letter appears an odd number of times.

• For example, "ccjjc" and "abab" are wonderful, but "ab" is not.

Given a string word that consists of the first ten lowercase English letters ('a' through 'j'), return the number of wonderful non-empty substrings in word. If the same substring appears multiple times in word, then count each occurrence separately.

substring is a contiguous sequence of characters in a string.

Example 1:

Input: word = "aba"
Output: 4
Explanation: The four wonderful substrings are underlined below:
- "aba" -> "a"
- "aba" -> "b"
- "aba" -> "a"
- "aba" -> "aba"


Example 2:

Input: word = "aabb"
Output: 9
Explanation: The nine wonderful substrings are underlined below:
- "aabb" -> "a"
- "aabb" -> "aa"
- "aabb" -> "aab"
- "aabb" -> "aabb"
- "aabb" -> "a"
- "aabb" -> "abb"
- "aabb" -> "b"
- "aabb" -> "bb"
- "aabb" -> "b"


Example 3:

Input: word = "he"
Output: 2
Explanation: The two wonderful substrings are underlined below:
- "he" -> "h"
- "he" -> "e"


Constraints:

• 1 <= word.length <= 105
• word consists of lowercase English letters from 'a' to 'j'.

## Solution: Prefix Bitmask + Hashtable

Similar to 花花酱 LeetCode 1371. Find the Longest Substring Containing Vowels in Even Counts, we use a bitmask to represent the occurrence (odd or even) of each letter and use a hashtable to store the frequency of each bitmask seen so far.

1. “0000000000” means all letters occur even times.
2. “0000000101” means all letters occur even times expect letter ‘a’ and ‘c’ that occur odd times.

We scan the word from left to right and update the bitmask: bitmask ^= (1 << (c-‘a’)).
However, the bitmask only represents the state of the prefix, i.e. word[0:i], then how can we count substrings? The answer is hashtable. If the same bitmask occurs c times before, which means there are c indices that word[0~j1], word[0~j2], …, word[0~jc] have the same state as word[0~i] that means for word[j1+1~i], word[j2+1~i], …, word[jc+1~i], all letters occurred even times.
For the “at most one odd” case, we toggle each bit of the bitmask and check how many times it occurred before.

ans += freq[mask] + sum(freq[mask ^ (1 << i)] for i in range(k))

Time complexity: O(n*k)
Space complexity: O(2k)
where k = j – a + 1 = 10

## C++

You and a gang of thieves are planning on robbing a bank. You are given a 0-indexed integer array security, where security[i] is the number of guards on duty on the ith day. The days are numbered starting from 0. You are also given an integer time.

The ith day is a good day to rob the bank if:

• There are at least time days before and after the ith day,
• The number of guards at the bank for the time days before i are non-increasing, and
• The number of guards at the bank for the time days after i are non-decreasing.

More formally, this means day i is a good day to rob the bank if and only if security[i - time] >= security[i - time + 1] >= ... >= security[i] <= ... <= security[i + time - 1] <= security[i + time].

Return a list of all days (0-indexed) that are good days to rob the bank. The order that the days are returned in does not matter.

Example 1:

Input: security = [5,3,3,3,5,6,2], time = 2
Output: [2,3]
Explanation:
On day 2, we have security[0] >= security[1] >= security[2] <= security[3] <= security[4].
On day 3, we have security[1] >= security[2] >= security[3] <= security[4] <= security[5].
No other days satisfy this condition, so days 2 and 3 are the only good days to rob the bank.


Example 2:

Input: security = [1,1,1,1,1], time = 0
Output: [0,1,2,3,4]
Explanation:
Since time equals 0, every day is a good day to rob the bank, so return every day.


Example 3:

Input: security = [1,2,3,4,5,6], time = 2
Output: []
Explanation:
No day has 2 days before it that have a non-increasing number of guards.
Thus, no day is a good day to rob the bank, so return an empty list.


Example 4:

Input: security = [1], time = 5
Output: []
Explanation:
No day has 5 days before and after it.
Thus, no day is a good day to rob the bank, so return an empty list.

Constraints:

• 1 <= security.length <= 105
• 0 <= security[i], time <= 105

## Solution: Pre-Processing

Pre-compute the non-increasing days at days[i] and the non-decreasing days at days[i] using prefix and suffix arrays.

Time complexity: O(n)
Space complexity: O(n)

## C++

Given two integers left and right that represent the range [left, right], return the bitwise AND of all numbers in this range, inclusive.

Example 1:

Input: left = 5, right = 7
Output: 4


Example 2:

Input: left = 0, right = 0
Output: 0


Example 3:

Input: left = 1, right = 2147483647
Output: 0


Constraints:

• 0 <= left <= right <= 231 - 1

## Solution: Bit operation

Bitwise AND all the numbers between left and right will clear out all the low bits. Basically this question is asking to find the common prefix of left and right in the binary format.

5 = 0b0101
7 = 0b0111
the common prefix is 0b0100 which is 4.

Time complexity: O(logn)
Space complexity: O(1)

## C++

You are given a sorted array nums of n non-negative integers and an integer maximumBit. You want to perform the following query n times:

1. Find a non-negative integer k < 2maximumBit such that nums[0] XOR nums[1] XOR ... XOR nums[nums.length-1] XOR k is maximizedk is the answer to the ith query.
2. Remove the last element from the current array nums.

Return an array answer, where answer[i] is the answer to the ith query.

Example 1:

Input: nums = [0,1,1,3], maximumBit = 2
Output: [0,3,2,3]
Explanation: The queries are answered as follows:
1st query: nums = [0,1,1,3], k = 0 since 0 XOR 1 XOR 1 XOR 3 XOR 0 = 3.
2nd query: nums = [0,1,1], k = 3 since 0 XOR 1 XOR 1 XOR 3 = 3.
3rd query: nums = [0,1], k = 2 since 0 XOR 1 XOR 2 = 3.
4th query: nums = [0], k = 3 since 0 XOR 3 = 3.


Example 2:

Input: nums = [2,3,4,7], maximumBit = 3
Output: [5,2,6,5]
Explanation: The queries are answered as follows:
1st query: nums = [2,3,4,7], k = 5 since 2 XOR 3 XOR 4 XOR 7 XOR 5 = 7.
2nd query: nums = [2,3,4], k = 2 since 2 XOR 3 XOR 4 XOR 2 = 7.
3rd query: nums = [2,3], k = 6 since 2 XOR 3 XOR 6 = 7.
4th query: nums = [2], k = 5 since 2 XOR 5 = 7.


Example 3:

Input: nums = [0,1,2,2,5,7], maximumBit = 3
Output: [4,3,6,4,6,7]


Constraints:

• nums.length == n
• 1 <= n <= 105
• 1 <= maximumBit <= 20
• 0 <= nums[i] < 2maximumBit
• nums​​​ is sorted in ascending order.

## Solution: Prefix XOR

Compute s = nums[0] ^ nums[1] ^ … nums[n-1] first

to remove nums[i], we just need to do s ^= nums[i]

We can always maximize the xor of s and k to (2^maxbit – 1)
k = (2 ^ maxbit – 1) ^ s

Time complexity: O(n)
Space complexity: O(1)