LeetCode - 39. Combination Sum

本文详细解析了组合求和问题的解决方法,该问题要求从给定的集合中找到所有可能的组合,使得这些组合的元素之和等于指定的目标值。文章通过递归算法实现了这一目标,并提供了完整的C++代码实现。

39. Combination Sum 

Problem's Link

 ----------------------------------------------------------------------------

Mean: 

给你一个待选集合s和一个数n,让你找出集合s中相加之和为n的所有组合.(每个数可选多次)

analyse:

作此题需对递归有一定的理解.

Time complexity: O(N)

 

view code

/**
* -----------------------------------------------------------------
* Copyright (c) 2016 crazyacking.All rights reserved.
* -----------------------------------------------------------------
*       Author: crazyacking
*       Date  : 2016-03-05-18.56
*/
#include <queue>
#include <cstdio>
#include <set>
#include <string>
#include <stack>
#include <cmath>
#include <climits>
#include <map>
#include <cstdlib>
#include <iostream>
#include <vector>
#include <algorithm>
#include <cstring>
using namespace std;
typedef long long( LL);
typedef unsigned long long( ULL);
const double eps( 1e-8);

class Solution
{
public :
    vector < vector < int >> combinationSum( vector < int >& candidates , int target)
    {
        sort( candidates . begin (), candidates . end());
        vector < vector < int >> res;
        vector < int > combination;
        combinationSum( candidates , res , combination , target , 0);
        return res;
    }
private :
    void combinationSum( vector < int >& candidates , vector < vector < int >> & res , vector < int >& combination , int target , int begin)
    {
        if( ! target)
        {
            res . push_back( combination);
            return;
        }
        for( int i = begin; target >= candidates [ i ] && i < candidates . size() ; ++ i)
        {
            combination . push_back( candidates [ i ]);
            combinationSum( candidates , res , combination , target - candidates [ i ], i);
            combination . pop_back();
        }
    }
};

int main()
{
    freopen( "H: \\ Code_Fantasy \\ in.txt" , "r" , stdin);
    int n , target;
    while( cin >>n >> target)
    {
        cout <<n << " " << target << endl;
        vector < int > ve;
        for( int i = 0; i <n; ++ i)
        {
            int tmp;
            cin >> tmp;
            ve . push_back( tmp);
        }
        Solution solution;
        vector < vector < int >> ans = solution . combinationSum( ve , target);
        for( auto p1: ans)
        {
            for( auto p2: p1)
            {
                cout << p2 << " ";
            }
            cout << endl;
        }
    }
    return 0;
}
/*

*/

转载于:https://www.cnblogs.com/crazyacking/p/5245786.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
### LeetCode Top 100 Popular Problems LeetCode provides an extensive collection of algorithmic challenges designed to help developers prepare for technical interviews and enhance their problem-solving skills. The platform categorizes these problems based on popularity, difficulty level, and frequency asked during tech interviews. The following list represents a curated selection of the most frequently practiced 100 problems from LeetCode: #### Array & String Manipulation 1. Two Sum[^2] 2. Add Two Numbers (Linked List)[^2] 3. Longest Substring Without Repeating Characters #### Dynamic Programming 4. Climbing Stairs 5. Coin Change 6. House Robber #### Depth-First Search (DFS) / Breadth-First Search (BFS) 7. Binary Tree Level Order Traversal[^3] 8. Surrounded Regions 9. Number of Islands #### Backtracking 10. Combination Sum 11. Subsets 12. Permutations #### Greedy Algorithms 13. Jump Game 14. Gas Station 15. Task Scheduler #### Sliding Window Technique 16. Minimum Size Subarray Sum 17. Longest Repeating Character Replacement #### Bit Manipulation 18. Single Number[^1] 19. Maximum Product of Word Lengths 20. Reverse Bits This list continues up until reaching approximately 100 items covering various categories including but not limited to Trees, Graphs, Sorting, Searching, Math, Design Patterns, etc.. Each category contains multiple representative questions that cover fundamental concepts as well as advanced techniques required by leading technology companies when conducting software engineering candidate assessments. For those interested in improving logical thinking through gaming activities outside traditional study methods, certain types of video games have been shown beneficial effects similar to engaging directly within competitive coding platforms [^4]. --related questions-- 1. How does participating in online coding competitions benefit personal development? 2. What specific advantages do DFS/BFS algorithms offer compared to other traversal strategies? 3. Can you provide examples illustrating how bit manipulation improves performance efficiency? 4. In what ways might regular participation in programming contests influence job interview success rates? 5. Are there any notable differences between solving problems on paper versus implementing solutions programmatically?
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