Posts tagged as “binary search”

You are given a 0-indexed array arr consisting of n positive integers, and a positive integer k.

The array arr is called K-increasing if arr[i-k] <= arr[i] holds for every index i, where k <= i <= n-1.

• For example, arr = [4, 1, 5, 2, 6, 2] is K-increasing for k = 2 because:
• arr[0] <= arr[2] (4 <= 5)
• arr[1] <= arr[3] (1 <= 2)
• arr[2] <= arr[4] (5 <= 6)
• arr[3] <= arr[5] (2 <= 2)
• However, the same arr is not K-increasing for k = 1 (because arr[0] > arr[1]) or k = 3 (because arr[0] > arr[3]).

In one operation, you can choose an index i and change arr[i] into any positive integer.

Return the minimum number of operations required to make the array K-increasing for the given k.

Example 1:

Input: arr = [5,4,3,2,1], k = 1
Output: 4
Explanation:
For k = 1, the resultant array has to be non-decreasing.
Some of the K-increasing arrays that can be formed are [5,6,7,8,9], [1,1,1,1,1], [2,2,3,4,4]. All of them require 4 operations.
It is suboptimal to change the array to, for example, [6,7,8,9,10] because it would take 5 operations.
It can be shown that we cannot make the array K-increasing in less than 4 operations.


Example 2:

Input: arr = [4,1,5,2,6,2], k = 2
Output: 0
Explanation:
This is the same example as the one in the problem description.
Here, for every index i where 2 <= i <= 5, arr[i-2] <=arr[i].
Since the given array is already K-increasing, we do not need to perform any operations.

Example 3:

Input: arr = [4,1,5,2,6,2], k = 3
Output: 2
Explanation:
Indices 3 and 5 are the only ones not satisfying arr[i-3] <= arr[i] for 3 <= i <= 5.
One of the ways we can make the array K-increasing is by changing arr[3] to 4 and arr[5] to 5.
The array will now be [4,1,5,4,6,5].
Note that there can be other ways to make the array K-increasing, but none of them require less than 2 operations.


Constraints:

• 1 <= arr.length <= 105
• 1 <= arr[i], k <= arr.length

Solution: Longest increasing subsequence

if k = 1, we need to modify the following arrays
1. [a[0], a[1], a[2], …]
if k = 2, we need to modify the following arrays
1. [a[0], a[2], a[4], …]
2. [a[1], a[3], a[5], …]
if k = 3, we need to modify the following arrays
1. [a[0], a[3], a[6], …]
2. [a[1], a[4], a[7], …]
3. [a[2], a[5], a[8], …]

These arrays are independent of each other, we just need to find LIS of it, # ops = len(arr) – LIS(arr).
Ans = sum(len(arri) – LIS(arri)) 1 <= i <= k

Time complexity: O(k * (n/k)* log(n/k)) = O(n * log(n/k))
Space complexity: O(n/k)

Python3

There is an integer array nums sorted in non-decreasing order (not necessarily with distinct values).

Before being passed to your function, nums is rotated at an unknown pivot index k (0 <= k < nums.length) such that the resulting array is [nums[k], nums[k+1], ..., nums[n-1], nums[0], nums[1], ..., nums[k-1]] (0-indexed). For example, [0,1,2,4,4,4,5,6,6,7] might be rotated at pivot index 5 and become [4,5,6,6,7,0,1,2,4,4].

Given the array nums after the rotation and an integer target, return true if target is in nums, or false if it is not in nums.

You must decrease the overall operation steps as much as possible.

Example 1:

Input: nums = [2,5,6,0,0,1,2], target = 0
Output: true


Example 2:

Input: nums = [2,5,6,0,0,1,2], target = 3
Output: false


Constraints:

• 1 <= nums.length <= 5000
• -104 <= nums[i] <= 104
• nums is guaranteed to be rotated at some pivot.
• -104 <= target <= 104

Solution: Binary search or divide and conquer

If current range is ordered, use binary search, Otherwise, divide and conquer.

Time complexity: O(logn) best, O(n) worst
Space complexity: O(logn)

C++

Given a non-negative integer x, compute and return the square root of x.

Since the return type is an integer, the decimal digits are truncated, and only the integer part of the result is returned.

Note: You are not allowed to use any built-in exponent function or operator, such as pow(x, 0.5) or x ** 0.5.

Example 1:

Input: x = 4
Output: 2


Example 2:

Input: x = 8
Output: 2
Explanation: The square root of 8 is 2.82842..., and since the decimal part is truncated, 2 is returned.

Constraints:

• 0 <= x <= 231 - 1

Solution 1: Binary Search

Find the smallest l such that l * l > x, sqrt(x) = l – 1.

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

C++

Given an array of integers nums sorted in non-decreasing order, find the starting and ending position of a given target value.

If target is not found in the array, return [-1, -1].

You must write an algorithm with O(log n) runtime complexity.

Example 1:

Input: nums = [5,7,7,8,8,10], target = 8
Output: [3,4]


Example 2:

Input: nums = [5,7,7,8,8,10], target = 6
Output: [-1,-1]


Example 3:

Input: nums = [], target = 0
Output: [-1,-1]


Constraints:

• 0 <= nums.length <= 105
• -109 <= nums[i] <= 109
• nums is a non-decreasing array.
• -109 <= target <= 109

Solution: Binary Search

Use lower_bound to find first
Use upper_bound to find last + 1

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

C++

There is an integer array nums sorted in ascending order (with distinct values).

Prior to being passed to your function, nums is possibly rotated at an unknown pivot index k (1 <= k < nums.length) such that the resulting array is [nums[k], nums[k+1], ..., nums[n-1], nums[0], nums[1], ..., nums[k-1]] (0-indexed). For example, [0,1,2,4,5,6,7] might be rotated at pivot index 3 and become [4,5,6,7,0,1,2].

Given the array nums after the possible rotation and an integer target, return the index of target if it is in nums, or -1 if it is not in nums.

You must write an algorithm with O(log n) runtime complexity.

Example 1:

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


Example 2:

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


Example 3:

Input: nums = [1], target = 0
Output: -1


Constraints:

• 1 <= nums.length <= 5000
• -104 <= nums[i] <= 104
• All values of nums are unique.
• nums is an ascending array that is possibly rotated.
• -104 <= target <= 104

Solution: Binary Search

If the current range [l, r] is ordered, reduce to normal binary search. Otherwise, determine the range to search next by comparing target and nums[0].

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