- 資料結構與演算法
- DSA - 首頁
- DSA - 概述
- DSA - 環境搭建
- DSA - 演算法基礎
- DSA - 漸近分析
- 資料結構
- DSA - 資料結構基礎
- DSA - 資料結構和型別
- DSA - 陣列資料結構
- 連結串列
- DSA - 連結串列資料結構
- DSA - 雙向連結串列資料結構
- DSA - 迴圈連結串列資料結構
- 棧與佇列
- DSA - 棧資料結構
- DSA - 表示式解析
- DSA - 佇列資料結構
- 搜尋演算法
- DSA - 搜尋演算法
- DSA - 線性搜尋演算法
- DSA - 二分搜尋演算法
- DSA - 插值搜尋
- DSA - 跳躍搜尋演算法
- DSA - 指數搜尋
- DSA - 斐波那契搜尋
- DSA - 子列表搜尋
- DSA - 雜湊表
- 排序演算法
- DSA - 排序演算法
- DSA - 氣泡排序演算法
- DSA - 插入排序演算法
- DSA - 選擇排序演算法
- DSA - 歸併排序演算法
- DSA - 希爾排序演算法
- DSA - 堆排序
- DSA - 桶排序演算法
- DSA - 計數排序演算法
- DSA - 基數排序演算法
- DSA - 快速排序演算法
- 圖資料結構
- DSA - 圖資料結構
- DSA - 深度優先遍歷
- DSA - 廣度優先遍歷
- DSA - 生成樹
- 樹資料結構
- DSA - 樹資料結構
- DSA - 樹的遍歷
- DSA - 二叉搜尋樹
- DSA - AVL樹
- DSA - 紅黑樹
- DSA - B樹
- DSA - B+樹
- DSA - 伸展樹
- DSA - 字典樹
- DSA - 堆資料結構
- 遞迴
- DSA - 遞迴演算法
- DSA - 使用遞迴實現漢諾塔
- DSA - 使用遞迴實現斐波那契數列
- 分治法
- DSA - 分治法
- DSA - 最大最小值問題
- DSA - Strassen矩陣乘法
- DSA - Karatsuba演算法
- 貪心演算法
- DSA - 貪心演算法
- DSA - 旅行商問題(貪心法)
- DSA - Prim最小生成樹
- DSA - Kruskal最小生成樹
- DSA - Dijkstra最短路徑演算法
- DSA - 地圖著色演算法
- DSA - 分數揹包問題
- DSA - 帶截止期限的作業排序
- DSA - 最優合併模式演算法
- 動態規劃
- DSA - 動態規劃
- DSA - 矩陣鏈乘法
- DSA - Floyd-Warshall演算法
- DSA - 0-1揹包問題
- DSA - 最長公共子序列演算法
- DSA - 旅行商問題(動態規劃法)
- 近似演算法
- DSA - 近似演算法
- DSA - 頂點覆蓋演算法
- DSA - 集合覆蓋問題
- DSA - 旅行商問題(近似演算法)
- 隨機化演算法
- DSA - 隨機化演算法
- DSA - 隨機化快速排序演算法
- DSA - Karger最小割演算法
- DSA - Fisher-Yates洗牌演算法
- DSA有用資源
- DSA - 問答
- DSA - 快速指南
- DSA -有用資源
- DSA - 討論
最大最小值問題
讓我們考慮一個可以用分治法解決的簡單問題。
最大最小值問題
演算法分析中的最大最小值問題是在陣列中查詢最大值和最小值。
解決方案
為了在一個大小為n的給定陣列numbers[]中找到最大和最小數字,可以使用以下演算法。首先,我們介紹樸素方法,然後介紹分治法。
樸素方法
樸素方法是解決任何問題的基本方法。在這種方法中,可以分別找到最大和最小數字。為了找到最大和最小數字,可以使用以下簡單的演算法。
Algorithm: Max-Min-Element (numbers[])
max := numbers[1]
min := numbers[1]
for i = 2 to n do
if numbers[i] > max then
max := numbers[i]
if numbers[i] < min then
min := numbers[i]
return (max, min)
示例
以下是上述方法在各種程式語言中的實現:
#include <stdio.h>
struct Pair {
int max;
int min;
};
// Function to find maximum and minimum using the naive algorithm
struct Pair maxMinNaive(int arr[], int n) {
struct Pair result;
result.max = arr[0];
result.min = arr[0];
// Loop through the array to find the maximum and minimum values
for (int i = 1; i < n; i++) {
if (arr[i] > result.max) {
result.max = arr[i]; // Update the maximum value if a larger element is found
}
if (arr[i] < result.min) {
result.min = arr[i]; // Update the minimum value if a smaller element is found
}
}
return result; // Return the pair of maximum and minimum values
}
int main() {
int arr[] = {6, 4, 26, 14, 33, 64, 46};
int n = sizeof(arr) / sizeof(arr[0]);
struct Pair result = maxMinNaive(arr, n);
printf("Maximum element is: %d\n", result.max);
printf("Minimum element is: %d\n", result.min);
return 0;
}
輸出
Maximum element is: 64 Minimum element is: 4
#include <iostream>
using namespace std;
struct Pair {
int max;
int min;
};
// Function to find maximum and minimum using the naive algorithm
Pair maxMinNaive(int arr[], int n) {
Pair result;
result.max = arr[0];
result.min = arr[0];
// Loop through the array to find the maximum and minimum values
for (int i = 1; i < n; i++) {
if (arr[i] > result.max) {
result.max = arr[i]; // Update the maximum value if a larger element is found
}
if (arr[i] < result.min) {
result.min = arr[i]; // Update the minimum value if a smaller element is found
}
}
return result; // Return the pair of maximum and minimum values
}
int main() {
int arr[] = {6, 4, 26, 14, 33, 64, 46};
int n = sizeof(arr) / sizeof(arr[0]);
Pair result = maxMinNaive(arr, n);
cout << "Maximum element is: " << result.max << endl;
cout << "Minimum element is: " << result.min << endl;
return 0;
}
輸出
Maximum element is: 64 Minimum element is: 4
public class MaxMinNaive {
static class Pair {
int max;
int min;
}
// Function to find maximum and minimum using the naive algorithm
static Pair maxMinNaive(int[] arr) {
Pair result = new Pair();
result.max = arr[0];
result.min = arr[0];
// Loop through the array to find the maximum and minimum values
for (int i = 1; i < arr.length; i++) {
if (arr[i] > result.max) {
result.max = arr[i]; // Update the maximum value if a larger element is found
}
if (arr[i] < result.min) {
result.min = arr[i]; // Update the minimum value if a smaller element is found
}
}
return result; // Return the pair of maximum and minimum values
}
public static void main(String[] args) {
int[] arr = {6, 4, 26, 14, 33, 64, 46};
Pair result = maxMinNaive(arr);
System.out.println("Maximum element is: " + result.max);
System.out.println("Minimum element is: " + result.min);
}
}
輸出
Maximum element is: 64 Minimum element is: 4
def max_min_naive(arr):
max_val = arr[0]
min_val = arr[0]
# Loop through the array to find the maximum and minimum values
for i in range(1, len(arr)):
if arr[i] > max_val:
max_val = arr[i] # Update the maximum value if a larger element is found
if arr[i] < min_val:
min_val = arr[i] # Update the minimum value if a smaller element is found
return max_val, min_val # Return the pair of maximum and minimum values
arr = [6, 4, 26, 14, 33, 64, 46]
max_val, min_val = max_min_naive(arr)
print("Maximum element is:", max_val)
print("Minimum element is:", min_val)
輸出
Maximum element is: 64 Minimum element is: 4
分析
樸素方法中的比較次數為2n - 2。
可以使用分治法減少比較次數。以下是該技術。
分治法
在這種方法中,陣列被分成兩半。然後使用遞迴方法找到每一半中的最大和最小數字。之後,返回每一半的兩個最大值中的最大值和兩個最小值中的最小值。
在這個給定的問題中,陣列中的元素個數為$y - x + 1$,其中y大於或等於x。
$\mathbf{\mathit{Max - Min(x, y)}}$ 將返回陣列$\mathbf{\mathit{numbers[x...y]}}$的最大值和最小值。
Algorithm: Max - Min(x, y) if y – x ≤ 1 then return (max(numbers[x],numbers[y]),min((numbers[x],numbers[y])) else (max1, min1):= maxmin(x, ⌊((x + y)/2)⌋) (max2, min2):= maxmin(⌊((x + y)/2) + 1)⌋,y) return (max(max1, max2), min(min1, min2))
示例
以下是上述方法在各種程式語言中的實現:
#include <stdio.h>
// Structure to store both maximum and minimum elements
struct Pair {
int max;
int min;
};
struct Pair maxMinDivideConquer(int arr[], int low, int high) {
struct Pair result;
struct Pair left;
struct Pair right;
int mid;
// If only one element in the array
if (low == high) {
result.max = arr[low];
result.min = arr[low];
return result;
}
// If there are two elements in the array
if (high == low + 1) {
if (arr[low] < arr[high]) {
result.min = arr[low];
result.max = arr[high];
} else {
result.min = arr[high];
result.max = arr[low];
}
return result;
}
// If there are more than two elements in the array
mid = (low + high) / 2;
left = maxMinDivideConquer(arr, low, mid);
right = maxMinDivideConquer(arr, mid + 1, high);
// Compare and get the maximum of both parts
result.max = (left.max > right.max) ? left.max : right.max;
// Compare and get the minimum of both parts
result.min = (left.min < right.min) ? left.min : right.min;
return result;
}
int main() {
int arr[] = {6, 4, 26, 14, 33, 64, 46};
int n = sizeof(arr) / sizeof(arr[0]);
struct Pair result = maxMinDivideConquer(arr, 0, n - 1);
printf("Maximum element is: %d\n", result.max);
printf("Minimum element is: %d\n", result.min);
return 0;
}
輸出
Maximum element is: 64 Minimum element is: 4
#include <iostream>
using namespace std;
// Structure to store both maximum and minimum elements
struct Pair {
int max;
int min;
};
Pair maxMinDivideConquer(int arr[], int low, int high) {
Pair result, left, right;
int mid;
// If only one element in the array
if (low == high) {
result.max = arr[low];
result.min = arr[low];
return result;
}
// If there are two elements in the array
if (high == low + 1) {
if (arr[low] < arr[high]) {
result.min = arr[low];
result.max = arr[high];
} else {
result.min = arr[high];
result.max = arr[low];
}
return result;
}
// If there are more than two elements in the array
mid = (low + high) / 2;
left = maxMinDivideConquer(arr, low, mid);
right = maxMinDivideConquer(arr, mid + 1, high);
// Compare and get the maximum of both parts
result.max = (left.max > right.max) ? left.max : right.max;
// Compare and get the minimum of both parts
result.min = (left.min < right.min) ? left.min : right.min;
return result;
}
int main() {
int arr[] = {6, 4, 26, 14, 33, 64, 46};
int n = sizeof(arr) / sizeof(arr[0]);
Pair result = maxMinDivideConquer(arr, 0, n - 1);
cout << "Maximum element is: " << result.max << endl;
cout << "Minimum element is: " << result.min << endl;
return 0;
}
輸出
Maximum element is: 64 Minimum element is: 4
public class MaxMinDivideConquer {
// Class to store both maximum and minimum elements
static class Pair {
int max;
int min;
}
static Pair maxMinDivideConquer(int[] arr, int low, int high) {
Pair result = new Pair();
Pair left, right;
int mid;
// If only one element in the array
if (low == high) {
result.max = arr[low];
result.min = arr[low];
return result;
}
// If there are two elements in the array
if (high == low + 1) {
if (arr[low] < arr[high]) {
result.min = arr[low];
result.max = arr[high];
} else {
result.min = arr[high];
result.max = arr[low];
}
return result;
}
// If there are more than two elements in the array
mid = (low + high) / 2;
left = maxMinDivideConquer(arr, low, mid);
right = maxMinDivideConquer(arr, mid + 1, high);
// Compare and get the maximum of both parts
result.max = Math.max(left.max, right.max);
// Compare and get the minimum of both parts
result.min = Math.min(left.min, right.min);
return result;
}
public static void main(String[] args) {
int[] arr = {6, 4, 26, 14, 33, 64, 46};
Pair result = maxMinDivideConquer(arr, 0, arr.length - 1);
System.out.println("Maximum element is: " + result.max);
System.out.println("Minimum element is: " + result.min);
}
}
輸出
Maximum element is: 64 Minimum element is: 4
def max_min_divide_conquer(arr, low, high):
# Structure to store both maximum and minimum elements
class Pair:
def __init__(self):
self.max = 0
self.min = 0
result = Pair()
# If only one element in the array
if low == high:
result.max = arr[low]
result.min = arr[low]
return result
# If there are two elements in the array
if high == low + 1:
if arr[low] < arr[high]:
result.min = arr[low]
result.max = arr[high]
else:
result.min = arr[high]
result.max = arr[low]
return result
# If there are more than two elements in the array
mid = (low + high) // 2
left = max_min_divide_conquer(arr, low, mid)
right = max_min_divide_conquer(arr, mid + 1, high)
# Compare and get the maximum of both parts
result.max = max(left.max, right.max)
# Compare and get the minimum of both parts
result.min = min(left.min, right.min)
return result
arr = [6, 4, 26, 14, 33, 64, 46]
result = max_min_divide_conquer(arr, 0, len(arr) - 1)
print("Maximum element is:", result.max)
print("Minimum element is:", result.min)
輸出
Maximum element is: 64 Minimum element is: 4
分析
設T(n)為$\mathbf{\mathit{Max - Min(x, y)}}$進行的比較次數,其中元素個數$n = y - x + 1$。
如果T(n)表示數字,則遞推關係可以表示為
$$T(n) = \begin{cases}T\left(\lfloor\frac{n}{2}\rfloor\right)+T\left(\lceil\frac{n}{2}\rceil\right)+2 & for\: n>2\\1 & for\:n = 2 \\0 & for\:n = 1\end{cases}$$
讓我們假設n是2的冪。因此,n = 2k,其中k是遞迴樹的高度。
所以,
$$T(n) = 2.T (\frac{n}{2}) + 2 = 2.\left(\begin{array}{c}2.T(\frac{n}{4}) + 2\end{array}\right) + 2 ..... = \frac{3n}{2} - 2$$
與樸素方法相比,在分治法中,比較次數較少。但是,使用漸近表示法,這兩種方法都用O(n)表示。