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feat: refactored docs structure
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+74
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---
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title: '153. Find Minimum in Rotated Sorted Array'
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description: 'Suppose an array of length n sorted in ascending order is rotated between 1 and n times. For example, the array nums = [0,1,2,4,5,6,7] might become:'
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icon: dot
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topics:
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- { name: "Array", slug: "array" }
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- { name: "Binary Search", slug: "binary-search" }
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video: "nIVW4P8b1VA"
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issue: "https://github.com/prdlk/leetcode/issues/62"
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sidebar:
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label: 'Find Minimum in Rotated Sorted Array'
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badge: 'Medium'
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---
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::::warning
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Notice that rotating an array [a[0], a[1], a[2], ..., a[n-1]] 1 time results in the array [a[n-1], a[0], a[1], a[2], ..., a[n-2]].
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::::
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### Example 1:
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- Input: `nums = [3,4,5,1,2]`
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- Output: `1`
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- Explanation: The original array was `[1,2,3,4,5]` rotated `3` times.
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### Example 2:
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- Input: `nums = [4,5,6,7,0,1,2]`
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- Output: `0`
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- Explanation: The original array was `[0,1,2,4,5,6,7]` and it was rotated `4` times.
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### Example 3:
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- Input: `nums = [11,13,15,17]`
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- Output: `11`
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- Explanation: The original array was `[11,13,15,17]` and it was rotated `4` times.
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### Constraints:
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- `n == nums.length`
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- `1 <= n <= 5000`
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- `-5000 <= nums[i] <= 5000`
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- All the integers of `nums` are unique.
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- `nums` is sorted and rotated between 1 and `n` times.
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## Approach
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```mermaid
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flowchart TD
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S(["findMin(nums)"]) --> I["l = 0, r = n-1, lowest_index = -1"]
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I --> W{"l <= r?"}
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W -- no --> E(["return nums[lowest_index]"])
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W -- yes --> M["m = (l + r) // 2"]
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M --> C{"nums[m] <= nums[-1]?"}
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C -- yes --> A["in the right sorted run: record lowest_index = m, r = m - 1"]
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A --> W
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C -- no --> B["still in the left sorted run: l = m + 1"]
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B --> W
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```
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## Solution
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```py
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class Solution:
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def findMin(self, nums: List[int]) -> int:
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l, r = 0, len(nums) - 1
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lowest_index = -1
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while l <= r:
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m = (l + r) // 2
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if nums[m] <= nums[-1]:
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lowest_index = m
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r = m - 1
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else:
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l = m + 1
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return nums[lowest_index]
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```
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+48
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---
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title: '162. Find Peak Element'
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description: A peak element is an element that is strictly greater than its neighbors
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icon: dot
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topics:
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- { name: "Array", slug: "array" }
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- { name: "Binary Search", slug: "binary-search" }
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issue: "https://github.com/prdlk/leetcode/issues/64"
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sidebar:
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label: 'Find Peak Element'
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badge: 'Medium'
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---
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::::warning
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You must write an algorithm that runs in O(log n) time.
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::::
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### Example 1:
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- Input: `nums = [1,2,3,1]`
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- Output: `2`
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- Explanation: `3` is a peak element and your function should return the index number `2`.
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### Example 2:
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- Input: `nums = [1,2,1,3,5,6,4]`
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- Output: `5`
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- Explanation: Your function can return either index number `1` where the peak element is `2`, or index number `5` where the peak element is `6`.
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### Constraints:
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- `1 <= nums.length <= 1000`
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- `-2^31 <= nums[i] <= 2^31 - 1`
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- `nums[i] != nums[i + 1] for all valid i.`
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## Solution
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```py
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class Solution:
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def findPeakElement(self, nums: list[int]) -> int:
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left, right = 0, len(nums) - 1
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while left < right:
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mid = (left + right) // 2
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if nums[mid] < nums[mid + 1]:
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left = mid + 1
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else:
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right = mid
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return left
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```
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---
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title: '33. Search in Rotated Sorted Array'
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description: There is an integer array nums sorted in ascending order (with distinct values)
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icon: dot
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topics:
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- { name: "Array", slug: "array" }
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- { name: "Binary Search", slug: "binary-search" }
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video: "U8XENwh8Oy8"
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issue: "https://github.com/prdlk/leetcode/issues/60"
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sidebar:
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label: 'Search in Rotated Sorted Array'
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badge: 'Medium'
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---
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::::warning
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You must write an algorithm with O(log n) runtime complexity.
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::::
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|
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### Example 1:
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- Input: `nums = [4,5,6,7,0,1,2], target = 0`
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- Output: `4`
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### Example 2:
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- Input: `nums = [4,5,6,7,0,1,2], target = 3`
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- Output: `-1`
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### Example 3:
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- Input: `nums = [1], target = 0`
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- Output: `-1`
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### Constraints:
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- `1 <= nums.length <= 5000`
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- `-10^4 <= nums[i] <= 10^4`
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- All values of `nums` are unique.
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- `nums` is an ascending array that is possibly rotated.
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- `-10^4 <= target <= 10^4`
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## Approach
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```mermaid
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flowchart TD
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S(["search(nums, target)"]) --> I["l = 0, r = n - 1"]
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I --> W{"l <= r?"}
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W -- no --> E(["return -1"])
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W -- yes --> M["mid = floor((l + r) / 2)"]
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M --> F{"nums[mid] == target?"}
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F -- yes --> R(["return mid"])
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F -- no --> H{"nums[l] <= nums[mid]? — left half sorted"}
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H -- yes --> A{"target outside [nums[l], nums[mid]]?"}
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A -- yes --> A1["l = mid + 1 — search the right half"]
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A -- no --> A2["r = mid - 1"]
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H -- no --> B{"target outside [nums[mid], nums[r]]?"}
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B -- yes --> B1["r = mid - 1 — search the left half"]
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B -- no --> B2["l = mid + 1"]
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A1 --> W
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A2 --> W
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B1 --> W
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B2 --> W
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```
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## Solution
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```js
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/**
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* @param {number[]} nums
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* @param {number} target
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* @return {number}
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*/
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var search = function(nums, target) {
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let l = 0, r = nums.length - 1;
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while (l <= r){
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let mid = Math.floor((l + r)/2);
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if (target === nums[mid]) {
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return mid
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}
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// Left sorted portion
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if (nums[l] <= nums[mid]){
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if (target > nums[mid] || target < nums[l]){
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l = mid + 1;
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}else{
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r = mid - 1;
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}
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}
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// Right sorted portion
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else{
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if(target < nums[mid] || target > nums[r]){
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r = mid - 1;
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}else{
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l = mid + 1;
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}
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}
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}
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return -1;
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};
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```
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+60
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---
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title: '34. Find First and Last Position of Element in Sorted Array'
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description: Given an array of integers nums sorted in non-decreasing order, find the starting and ending position of a given target value
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icon: dot
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topics:
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- { name: "Array", slug: "array" }
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- { name: "Binary Search", slug: "binary-search" }
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issue: "https://github.com/prdlk/leetcode/issues/65"
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sidebar:
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label: 'Find First and Last Position of Element in Sorted Array'
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badge: 'Medium'
|
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---
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|
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::::warning
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You must write an algorithm with O(log n) runtime complexity.
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::::
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|
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### Example 1:
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- Input: `nums = [5,7,7,8,8,10], target = 8`
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- Output: `[3,4]`
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### Example 2:
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- Input: `nums = [5,7,7,8,8,10], target = 6`
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- Output: `[-1,-1]`
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### Example 3:
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- Input: `nums = [], target = 0`
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- Output: `[-1,-1]`
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### Constraints:
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- `0 <= nums.length <= 10^5`
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- `-10^9 <= nums[i] <= 10^9`
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- `nums` is a non-decreasing array.
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- `-10^9 <= target <= 10^9`
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## Solution
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```py
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class Solution:
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def searchRange(self, nums: list[int], target: int) -> list[int]:
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def find(first):
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result = -1
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left, right = 0, len(nums) - 1
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while left <= right:
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mid = (left + right) // 2
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if nums[mid] == target:
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result = mid
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if first:
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right = mid - 1
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else:
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left = mid + 1
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elif nums[mid] < target:
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left = mid + 1
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else:
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right = mid - 1
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return result
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return [find(True), find(False)]
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```
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+71
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---
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title: '35. Search Insert Position'
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description: Given a sorted array of distinct integers and a target value, return the index if the target is found. If not, return the index where it would be if it were inserted in order
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icon: dot
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topics:
|
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- { name: "Array", slug: "array" }
|
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- { name: "Binary Search", slug: "binary-search" }
|
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video: "K-RYzDZkzCI"
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issue: "https://github.com/prdlk/leetcode/issues/63"
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sidebar:
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label: 'Search Insert Position'
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badge: 'Easy'
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---
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::::warning
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You must write an algorithm with O(log n) runtime complexity.
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::::
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||||
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### Example 1:
|
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- Input: `nums = [1,3,5,6], target = 5`
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- Output: `2`
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### Example 2:
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- Input: `nums = [1,3,5,6], target = 2`
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- Output: `1`
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### Example 3:
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- Input: `nums = [1,3,5,6], target = 7`
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- Output: `4`
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### Constraints:
|
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|
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- `1 <= nums.length <= 10^4`
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- `-10^4 <= nums[i] <= 10^4`
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- `nums` contains distinct values sorted in ascending order.
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- `-10^4 <= target <= 10^4`
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## Approach
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```mermaid
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flowchart TD
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S(["searchInsert(nums, target)"]) --> I["l = 0, r = n - 1"]
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I --> W{"l <= r?"}
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W -- yes --> M["m = (l + r) // 2"]
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M --> Q{"nums[m] vs target"}
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Q -- equal --> R(["return m"])
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Q -- "nums[m] < target" --> L["l = m + 1"]
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L --> W
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Q -- "nums[m] > target" --> H["r = m - 1"]
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H --> W
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W -- no --> E(["return (l + r) // 2 + 1 — the insert slot"])
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```
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## Solution
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```py
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class Solution:
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def searchInsert(self, nums: List[int], target: int) -> int:
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l, r = 0, len(nums) - 1
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|
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while l <= r:
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m = (l + r) // 2
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if nums[m] == target:
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return m
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elif nums[m] < target:
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l = m + 1
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elif nums[m] > target:
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r = m - 1
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return (l + r) // 2 + 1
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```
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+39
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---
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title: '4. Median of Two Sorted Arrays'
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description: Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays
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icon: dot
|
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topics:
|
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- { name: "Array", slug: "array" }
|
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- { name: "Binary Search", slug: "binary-search" }
|
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- { name: "Divide and Conquer", slug: "divide-and-conquer" }
|
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issue: "https://github.com/prdlk/leetcode/issues/66"
|
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sidebar:
|
||||
label: 'Median of Two Sorted Arrays'
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badge: 'Hard'
|
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---
|
||||
|
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### Example 1:
|
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- Input: `nums1 = [1,3], nums2 = [2]`
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- Output: `2.00000`
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- Explanation: merged array = `[1,2,3]` and median is `2`.
|
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|
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### Example 2:
|
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- Input: `nums1 = [1,2], nums2 = [3,4]`
|
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- Output: `2.50000`
|
||||
- Explanation: merged array = `[1,2,3,4]` and median is `(2 + 3`) / `2 = 2.5`.
|
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|
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### Constraints:
|
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|
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- `nums1.length == m`
|
||||
- `nums2.length == n`
|
||||
- `0 <= m <= 1000`
|
||||
- `0 <= n <= 1000`
|
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- `1 <= m + n <= 2000`
|
||||
- `-10^6 <= nums1[i], nums2[i] <= 10^6`
|
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|
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## Solution
|
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|
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```py
|
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class Solution:
|
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def findMedianSortedArrays(self, nums1: list[int], nums2: list[int]) -> float:
|
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```
|
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+76
@@ -0,0 +1,76 @@
|
||||
---
|
||||
title: '704. Binary Search'
|
||||
description: Given an array of integers nums which is sorted in ascending order, and an integer target, write a function to search target in nums. If target exists, then return its index. Otherwise, return -1
|
||||
icon: dot
|
||||
topics:
|
||||
- { name: "Array", slug: "array" }
|
||||
- { name: "Binary Search", slug: "binary-search" }
|
||||
video: "s4DPM8ct1pI"
|
||||
issue: "https://github.com/prdlk/leetcode/issues/58"
|
||||
sidebar:
|
||||
label: 'Binary Search'
|
||||
badge: 'Easy'
|
||||
---
|
||||
|
||||
::::warning
|
||||
You must write an algorithm with O(log n) runtime complexity.
|
||||
::::
|
||||
|
||||
### Example 1:
|
||||
- Input: `nums = [-1,0,3,5,9,12], target = 9`
|
||||
- Output: `4`
|
||||
- Explanation: `9` exists in `nums` and its index is `4`
|
||||
|
||||
### Example 2:
|
||||
- Input: `nums = [-1,0,3,5,9,12], target = 2`
|
||||
- Output: `-1`
|
||||
- Explanation: `2` does not exist in `nums` so return `-1`
|
||||
|
||||
### Constraints:
|
||||
|
||||
- `1 <= nums.length <= 10^4`
|
||||
- `-10^4 < nums[i], target < 10^4`
|
||||
- All the integers in `nums` are unique.
|
||||
- `nums` is sorted in ascending order.
|
||||
|
||||
## Approach
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
S(["search(nums, target)"]) --> I["left = 0, right = n - 1"]
|
||||
I --> W{"left <= right?"}
|
||||
W -- no --> E(["return -1"])
|
||||
W -- yes --> M["mid = left + floor((right - left) / 2)"]
|
||||
M --> Q{"nums[mid] vs target"}
|
||||
Q -- equal --> R(["return mid"])
|
||||
Q -- "nums[mid] < target" --> L["left = mid + 1"]
|
||||
L --> W
|
||||
Q -- "nums[mid] > target" --> H["right = mid - 1"]
|
||||
H --> W
|
||||
```
|
||||
|
||||
## Solution
|
||||
|
||||
```js
|
||||
/**
|
||||
* @param {number[]} nums
|
||||
* @param {number} target
|
||||
* @return {number}
|
||||
*/
|
||||
var search = function(nums, target) {
|
||||
let left = 0;
|
||||
let right = nums.length - 1;
|
||||
|
||||
while(left <= right) {
|
||||
const mid = left + Math.floor((right - left) / 2);
|
||||
if (nums[mid] === target) {
|
||||
return mid;
|
||||
} else if(nums[mid] < target){
|
||||
left = mid + 1;
|
||||
} else if (nums[mid] > target) {
|
||||
right = mid - 1;
|
||||
}
|
||||
}
|
||||
return -1;
|
||||
};
|
||||
```
|
||||
+92
@@ -0,0 +1,92 @@
|
||||
---
|
||||
title: '74. Search a 2D Matrix'
|
||||
description: 'You are given an m x n integer matrix matrix with the following two properties:'
|
||||
icon: dot
|
||||
topics:
|
||||
- { name: "Array", slug: "array" }
|
||||
- { name: "Binary Search", slug: "binary-search" }
|
||||
- { name: "Matrix", slug: "matrix" }
|
||||
video: "Ber2pi2C0j0"
|
||||
issue: "https://github.com/prdlk/leetcode/issues/61"
|
||||
sidebar:
|
||||
label: 'Search a 2D Matrix'
|
||||
badge: 'Medium'
|
||||
---
|
||||
|
||||
::::warning
|
||||
You must write a solution in O(log(m * n)) time complexity.
|
||||
::::
|
||||
|
||||
### Example 1:
|
||||
- Input: `matrix = [[1,3,5,7],[10,11,16,20],[23,30,34,60]], target = 3`
|
||||
- Output: `true`
|
||||
|
||||
### Example 2:
|
||||
- Input: `matrix = [[1,3,5,7],[10,11,16,20],[23,30,34,60]], target = 13`
|
||||
- Output: `false`
|
||||
|
||||
### Constraints:
|
||||
|
||||
- `m == matrix.length`
|
||||
- `n == matrix[i].length`
|
||||
- `1 <= m, n <= 100`
|
||||
- `-10^4 <= matrix[i][j], target <= 10^4`
|
||||
|
||||
## Approach
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
S(["searchMatrix(matrix, target)"]) --> I["top = 0, bot = ROWS - 1"]
|
||||
I --> W1{"top <= bot?"}
|
||||
W1 -- yes --> R1["row = (top + bot) // 2"]
|
||||
R1 --> C1{"target vs that row's range"}
|
||||
C1 -- "above matrix[row][-1]" --> U["top = row + 1"]
|
||||
C1 -- "below matrix[row][0]" --> V["bot = row - 1"]
|
||||
C1 -- inside --> B(["break — row found"])
|
||||
U --> W1
|
||||
V --> W1
|
||||
W1 -- no --> F(["return False — no row can hold it"])
|
||||
B --> P["row = (top + bot) // 2; l = 0, r = COLS - 1"]
|
||||
P --> W2{"l <= r?"}
|
||||
W2 -- no --> G(["return False"])
|
||||
W2 -- yes --> M["m = (l + r) // 2"]
|
||||
M --> C2{"target vs matrix[row][m]"}
|
||||
C2 -- greater --> X["l = m + 1"]
|
||||
C2 -- smaller --> Y["r = m - 1"]
|
||||
C2 -- equal --> T(["return True"])
|
||||
X --> W2
|
||||
Y --> W2
|
||||
```
|
||||
|
||||
## Solution
|
||||
|
||||
```py
|
||||
class Solution:
|
||||
def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:
|
||||
ROWS, COLS = len(matrix), len(matrix[0])
|
||||
|
||||
top, bot = 0, ROWS - 1
|
||||
while top <= bot:
|
||||
row = (top + bot) // 2
|
||||
if target > matrix[row][-1]:
|
||||
top = row + 1
|
||||
elif target < matrix[row][0]:
|
||||
bot = row - 1
|
||||
else:
|
||||
break
|
||||
|
||||
if not (top <= bot):
|
||||
return False
|
||||
|
||||
row = (top + bot) // 2
|
||||
l, r = 0, COLS - 1
|
||||
while l <= r:
|
||||
m = (l + r) // 2
|
||||
if target > matrix[row][m]:
|
||||
l = m + 1
|
||||
elif target < matrix[row][m]:
|
||||
r = m - 1
|
||||
else:
|
||||
return True
|
||||
return False
|
||||
```
|
||||
+82
@@ -0,0 +1,82 @@
|
||||
---
|
||||
title: '875. Koko Eating Bananas'
|
||||
description: Koko loves to eat bananas. There are n piles of bananas, the i^th pile has piles[i] bananas. The guards have gone and will come back in h hours
|
||||
icon: dot
|
||||
topics:
|
||||
- { name: "Array", slug: "array" }
|
||||
- { name: "Binary Search", slug: "binary-search" }
|
||||
video: "U2SozAs9RzA"
|
||||
issue: "https://github.com/prdlk/leetcode/issues/59"
|
||||
sidebar:
|
||||
label: 'Koko Eating Bananas'
|
||||
badge: 'Medium'
|
||||
---
|
||||
|
||||
### Example 1:
|
||||
- Input: `piles = [3,6,7,11], h = 8`
|
||||
- Output: `4`
|
||||
|
||||
### Example 2:
|
||||
- Input: `piles = [30,11,23,4,20], h = 5`
|
||||
- Output: `30`
|
||||
|
||||
### Example 3:
|
||||
- Input: `piles = [30,11,23,4,20], h = 6`
|
||||
- Output: `23`
|
||||
|
||||
### Constraints:
|
||||
|
||||
- `1 <= piles.length <= 10^4`
|
||||
- `piles.length <= h <= 10^9`
|
||||
- `1 <= piles[i] <= 10^9`
|
||||
|
||||
## Approach
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
S(["minEatingSpeed(piles, h)"]) --> I["l = 1, r = max(piles)"]
|
||||
I --> W{"l < r?"}
|
||||
W -- no --> E(["return l — the smallest workable speed"])
|
||||
W -- yes --> M["mid = floor((l + r) / 2)"]
|
||||
M --> K["kWorks(mid): hours = sum of ceil(p / mid)"]
|
||||
K --> Q{"hours <= h?"}
|
||||
Q -- yes --> A["r = mid — keep mid, try slower"]
|
||||
Q -- no --> B["l = mid + 1 — too slow, speed up"]
|
||||
A --> W
|
||||
B --> W
|
||||
```
|
||||
|
||||
## Solution
|
||||
|
||||
```js
|
||||
/**
|
||||
* @param {number[]} piles
|
||||
* @param {number} h
|
||||
* @return {number}
|
||||
*/
|
||||
var minEatingSpeed = function(piles, h) {
|
||||
// Function to determine if K works for the H
|
||||
function kWorks(k){
|
||||
let hours = 0;
|
||||
for (let p of piles){
|
||||
hours += Math.ceil(p/k)
|
||||
}
|
||||
return hours <= h;
|
||||
}
|
||||
|
||||
// Setup Binary Search
|
||||
let l = 1;
|
||||
let r = Math.max(...piles);
|
||||
|
||||
// Find the K value which works for H and consumes all bananas
|
||||
while (l < r){
|
||||
const mid = Math.floor((l + r) / 2);
|
||||
if (kWorks(mid)){
|
||||
r = mid;
|
||||
}else{
|
||||
l = mid + 1;
|
||||
}
|
||||
}
|
||||
return l;
|
||||
};
|
||||
```
|
||||
@@ -0,0 +1,86 @@
|
||||
---
|
||||
title: Binary Search
|
||||
description: 'Half of a sorted range is removed at each step until the target is found.'
|
||||
hidden: true
|
||||
---
|
||||
|
||||
Binary search is an efficient array search algorithm. It works by narrowing down the search range by half each time. If you have looked up a word in a physical dictionary, you've already used binary search in real life. Let's look at a simple example:
|
||||
|
||||
*Given a sorted array of integers and an integer called target, find the element that equals the target and return its index. If the element is not found, return -1.*
|
||||
|
||||
The key observation here is that the array is sorted. We pick a random element in the array and compare it to the target.
|
||||
|
||||
- If we happen to pick the element that equals the target (how lucky!), then bingo. We don't need to do any more work; we return its index.
|
||||
- If the element is smaller than the target, then we know the target cannot be found in the section to the left of the current element, since everything to the left is even smaller. So, we discard the current element and everything on the left from the search range.
|
||||
- If the element is larger than the target, then we know the target cannot be found in the section to the right of the current element, since everything to the right is even larger. So, we discard the current element and everything on the right from the search range.
|
||||
|
||||
We repeat this process until we find the target. Instead of picking a random element, we always pick the **middle** element in the current search range. This way, we can discard half of the options and shrink the search range by half each time. This gives us `O(log(N))` runtime.
|
||||
|
||||
This idea can be implemented both iteratively and recursively. However, the major difference is that the iterative version of binary search uses `O(1)` memory while the recursive version uses `O(log(N))` memory.
|
||||
|
||||
## Implementation
|
||||
|
||||
The search range is represented by the `left` and `right` indices that start from both ends of the array and move towards each other as we search. When moving the index, we discard elements and shrink the search range.
|
||||
|
||||
Time Complexity: `O(log(n))`
|
||||
|
||||
|
||||
<Carousel>
|
||||
<CarouselImage src='../../../public/binary_search.001.png' />
|
||||
<CarouselImage src='../../../public/binary_search.002.png' />
|
||||
<CarouselImage src='../../../public/binary_search.003.png' />
|
||||
<CarouselImage src='../../../public/binary_search.004.png' />
|
||||
<CarouselImage src='../../../public/binary_search.005.png' />
|
||||
<CarouselImage src='../../../public/binary_search.006.png' />
|
||||
<CarouselImage src='../../../public/binary_search.007.png' />
|
||||
<CarouselImage src='../../../public/binary_search.008.png' />
|
||||
</Carousel>
|
||||
|
||||
```python
|
||||
def binary_search(arr: list[int], target: int) -> int:
|
||||
left, right = 0, len(arr) - 1
|
||||
# <= because left and right could point to the same element, < would miss it
|
||||
while left <= right:
|
||||
# double slash for integer division in python 3,
|
||||
# we don't have to worry about integer `left + right` overflow
|
||||
# since python integers can be arbitrarily large
|
||||
mid = (left + right) // 2
|
||||
# found target, return its index
|
||||
if arr[mid] == target:
|
||||
return mid
|
||||
# middle less than target, discard left half by making left search boundary `mid + 1`
|
||||
if arr[mid] < target:
|
||||
left = mid + 1
|
||||
# middle greater than target, discard right half by making right search boundary `mid - 1`
|
||||
else:
|
||||
right = mid - 1
|
||||
return -1 # if we get here we didn't hit above return so we didn't find target
|
||||
```
|
||||
|
||||
### Calculating `mid`
|
||||
|
||||
Note that when calculating `mid`, if the number of elements is even, there are two elements in the middle. We usually follow the convention of picking the first one, equivalent to doing integer division `(left + right) / 2`.
|
||||
|
||||
In most programming languages, we calculate `mid` with `left + floor((right-left) / 2)` to avoid potential integer overflow. However, in Python, we do not need to worry about integer overflow with `left + right` because Python3 integers can be arbitrarily large.
|
||||
|
||||
## Deduction
|
||||
|
||||
It's essential to understand and deduce the algorithm yourself instead of memorizing it. In an actual interview, interviewers may ask you additional questions to test your understanding, so simply memorizing the algorithm may not be enough to convince the interviewer.
|
||||
|
||||
Key elements in writing a correct binary search:
|
||||
|
||||
### 1. When to terminate the loop
|
||||
|
||||
Make sure the `while` loop includes an equality comparison; otherwise, we might skip the loop and miss the potential match for the edge case of a one-element array.
|
||||
|
||||
### 2. Whether/how to update `left` and `right` boundary in the `if` conditions
|
||||
|
||||
Consider which side to discard. If `arr[mid]` is smaller than the `target`, we should discard everything on the left by making `left = mid + 1`.
|
||||
|
||||
### 3. Should I discard the current element?
|
||||
|
||||
For vanilla binary search, we can discard it since it cannot be the final answer if it is not equal to the target. There might be situations where you would want to think twice before discarding the current element. We'll discuss this in the next module.
|
||||
|
||||
## When to use binary search
|
||||
|
||||
Interestingly, binary search works beyond sorted arrays. **You can use binary search whenever you make a binary decision to shrink the search range.**
|
||||
@@ -0,0 +1,3 @@
|
||||
import { defineMeta } from "blume";
|
||||
|
||||
export default defineMeta({ title: "Binary Search", order: 5, icon: "search", display: "page" });
|
||||
Reference in new issue
Block a user