javascript中的leetcode 3sum

时间:2019-07-15 12:18:49

标签: javascript algorithm

这是leetcode中的3sum问题

这是解决方案之一:

/**
 * step 1: nums.length<3; return [];
 * step 2: every element can only be used once // var used=[]; if(used[i]){ continue ;}else{ used[i]=true; excute; }
 * step 3: [-1, -1, 2] [-1, 2, -1] [2, -1, -1], the same, // Elements in a solution (a1, a2, … , ak) must be in non-descending order. (ie, a1 ≤ a2 ≤ … ≤ ak).
 * step 4: backTracking function
 */
var threeSum = function(nums) {
    if(nums.length<3) { return []; } // step 1
    nums.sort(function(val1,val2) {
        return val1>val2?1:val1<val2?-1:0;
    });
    // console.log(nums);
    var solution = [];
    var result = [];
    var used = [];
    var backTracking = function(m, n){ // step 4
        if(m==n){
            // console.log(solution);
            if(solution[0]+solution[1]+solution[2]===0){
                return result.push(solution.slice(0));
            }
            return false;
        }else{
            var last_num;
            for(var i=0; i<nums.length; i++){
                if(used[i]) { continue; } // step 2
                if(last_num==nums[i]) { continue; }
                if(m>0 && solution[m-1]>nums[i]) { continue; } // step 3
                used[i] = true;
                last_num = nums[i];
                solution[m] = nums[i];
                arguments.callee(m+1, n);
                used[i] = false;
            }
        }
    }
    backTracking(0, 3);
    return result;
};

这是我尝试写的:

var threeSum = function(ns) {
    // < 3, empty
    if(ns.length < 3) return [];

    ns.sort((a, b) => {
        return a-b;
    });

    let used = new Array(ns.length).fill(false);
    bt(res=[], sol=[], used, ns, 0, 3);

    return res;
};

var bt = function(res, sol, used, ns, m, n) {

    //console.log(res, sol, used, ns, m, n)

    if(m === n) {
        if(sol.length === 3 && sol[0]+sol[1]+sol[2] === 0) {
            //console.log('push')
            return res.push(sol);
        }

        //console.log('re false');
        return false;
    } else {
        let lastNum;
        for(let i=0; i<ns.length; i++) {
            // each num used once only
            if(used[i]) continue;

            // not next each other
            if(lastNum === ns[i]) continue;

            // 1. [-1, -1, 2] [-1, 2, -1] [2, -1, -1], the same
            // 2. ns[i] >= sol[m-1], must be
            // 3. ns[i] < sol[m-1], con
            if(m>0 && sol[m-1] > ns[i]) continue;

            // hash
            used[i] = true;

            // prev #
            lastNum = ns[i];

            // into sol
            let sol1 = sol.concat(ns[i]);

            // recur
            bt(res, sol1, used, ns, m+1, n);

            // ????
            used[i] = false;
        }
    }
}

在此测试用例中失败:

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2 个答案:

答案 0 :(得分:0)

每次您要使用一个元素,直到使用为止。因此,您正在运行n ^ 3解决方案。

您遇到“超过时限”的情况包含约3000个元素。 因此,在这种情况下,您算法的复杂度约为3000 * 3000 * 3000 = 27 * 10 ^ 9,这太高了,无法被接受。

尝试将复杂度降低为O(n ^ 2)或O(n ^ 2log(n))

答案 1 :(得分:0)

您需要一种新算法;这是低效率的。

将列表按升序排列。您将嵌套循环以遍历非正数和非负数部分。

找到第一个0;将该位置称为“中心”。

solution = set()    # empty set
for neg in (center, 0, -1):    # iterate back to the list beginning
    for pos in (center+1, len(ns), 1):    # iterate to end of list
        need = ns(neg) + ns(pos)
        if need in ns:
            # sort elements and add to solution set
            solution.add( sorted(ns(neg), ns(pos), need) )

现在,我会为您解决问题。假设您有一个起始数组ns = [-20, -5, 0, 1, 4, 16, 20]。您很快就会在neg=0, pos=2, need=4找到一个解决方案,并将(-5,1,4)添加到您的解决方案集中。然后,您将迭代到neg=0, pos=3,它将在排序之前再次以(-5,4,1)查找解决方案。

在尝试处理任何此类冗余解决方案之前,如何检测到您已经在内部循环中通过了有用的点,并回到外部循环?