Tuesday, January 5, 2016

LeetCode 325 - Maximum Size Subarray Sum Equals k


http://buttercola.blogspot.com/2016/01/leetcode-maximum-size-subarray-sum.html
Given an array nums and a target value k, find the maximum length of a subarray that sums to k. If there isn't one, return 0 instead.
Example 1:
Given nums = [1, -1, 5, -2, 3]k = 3,
return 4. (because the subarray [1, -1, 5, -2] sums to 3 and is the longest)
Example 2:
Given nums = [-2, -1, 2, 1]k = 1,
return 2. (because the subarray [-1, 2] sums to 1 and is the longest)
Follow Up:
Can you do it in O(n) time?


Note the map.put(0, -1). We need to put this entry into the map before, because if the maximal range starts from 0, we need to calculate sum(j) - sum(i - 1). 
The problem is equal to: find out a range from i to j, in which the sum (nums[i], ..., nums[j]) = k. What is the maximal range? 

So we can first calculate the prefix sum of each number, so sum(i, j) = sum(j) - sum(i - 1) = k. Therefore, for each sum(j), we only need to check if there was a sum(i - 1) which equals to sum(j) - k. We can use a hash map to store the previous calculated sum. 
Also: https://fxrcode.gitbooks.io/leetcodenotebook/content/Leetcode_Medium/maximum_size_subarray_sum_equals_k.html


    public int maxSubArrayLen(int[] nums, int k) {
        if(nums == null || nums.length == 0) {
            return 0;
        }
         
        int maxLen = 0;
        Map<Integer, Integer> map = new HashMap<>();
        map.put(0, -1); // IMPOARTANT
        int sum = 0;
         
        for (int i = 0; i < nums.length; i++) {
            sum += nums[i];
            if (!map.containsKey(sum)) {
                map.put(sum, i);
            }
             
            if (map.containsKey(sum - k)) {
                maxLen = Math.max(maxLen, i - map.get(sum - k));
            }
        }
         
        return maxLen;
    }
https://leetcode.com/discuss/77879/o-n-super-clean-9-line-java-solution-with-hashmap
public int maxSubArrayLen(int[] nums, int k) {
    int sum = 0, max = 0;
    HashMap<Integer, Integer> map = new HashMap<Integer, Integer>();
    for (int i = 0; i < nums.length; i++) {
        sum = sum + nums[i];
        if (sum == k) max = i + 1;
        else if (map.containsKey(sum - k)) max = Math.max(max, i - map.get(sum - k));
        if (!map.containsKey(sum)) map.put(sum, i);
    }
    return max;
}
The HashMap stores the sum of all elements before index i as key, and i as value. For each i, check not only the current sum but also (currentSum - previousSum) to see if there is any that equals k, and update max length.
https://leetcode.com/discuss/78533/java-o-n-explain-how-i-come-up-with-this-idea
The subarray sum reminds me the range sum problem. Preprocess the input array such that you get the range sum in constant time. sum[i] means the sum from 0 to i inclusively the sum from i to j is sum[j] - sum[i - 1] except that from 0 to j is sum[j].
j-i is equal to the length of subarray of original array. we want to find the max(j - i) for any sum[j] we need to find if there is a previous sum[i] such that sum[j] - sum[i] = k Instead of scanning from 0 to j -1 to find such i, we use hashmap to do the job in constant time. However, there might be duplicate value of of sum[i] we should avoid overriding its index as we want the max j - i, so we want to keep i as left as possible.
public class Solution {
    public int maxSubArrayLen(int[] nums, int k) {
        if (nums == null || nums.length == 0)
            return 0;
        int n = nums.length;
        for (int i = 1; i < n; i++)
            nums[i] += nums[i - 1];
        Map<Integer, Integer> map = new HashMap<>();
        map.put(0, -1); // add this fake entry to make sum from 0 to j consistent
        int max = 0;
        for (int i = 0; i < n; i++) {
            if (map.containsKey(nums[i] - k))
                max = Math.max(max, i - map.get(nums[i] - k));
            if (!map.containsKey(nums[i])) // keep only 1st duplicate as we want first index as left as possible
                map.put(nums[i], i);
        }
        return max;
    }
}
http://algobox.org/maximum-size-subarray-sum-equals-k/
Subarray sum also known as Range sum can be defined as the sum of all elements in the range [i, j) where i < jdenoted by S(i, j). Note that in this definition, i is inclusive and j is exclusive.
When i = 0, we often call it prefix sum, denoted by S[j] = S(0, j). It is easy to see that
S(i, j) = S[j] – S[i] and 0<= i < n and 0 < j <= n
Therefore if we have all the prefix sums calculated in O(n) any query of S(i, j) is O(1).
This is exactly what we did in the problem Range sum query – Immutable
OK, now let’s consider this problem. Suppose we calculated all the prefix sums and stored it in an array sums then the problem becomes:
maximize j - i
subject to sums[j] - sums[i] = k
This is only a variant of the famous problem two sum. The solution is almost the same
This solution is quite fast, it runs in 24 ms in LeetCode. But this is not one pass. Similar to the two sum, we can do this in one pass too.
Despite its shortness and conciseness, it runs much slower, 36 ms, in LeetCode. HashMap operations are far slower than array in Java.
public int maxSubArrayLen(int[] nums, int k) {
    int n = nums.length, ans = 0, sum = 0;
    Map<Integer, Integer> map = new HashMap<>();
    for (int i = 0; i < n; ++i) {
        map.putIfAbsent(sum, i);
        sum += nums[i];
        if (map.containsKey(sum - k))
            ans = Math.max(ans, i + 1 - map.get(sum - k));
    }
    return ans;
}

http://www.cnblogs.com/EdwardLiu/p/5104280.html
http://www.cnblogs.com/vision-love-programming/p/5102494.html
    public int maxSubArrayLen(int[] nums, int k) {
        HashMap<Integer, Integer> map = new HashMap<Integer, Integer>();
        int sum = 0;
        int result = 0;
        map.put(0, -1);
        for (int i = 0; i < nums.length; i++) {
            sum += nums[i];
            if (map.containsKey(sum - k)) {
                result = Math.max(result, i - map.get(sum - k));
            }
            if (!map.containsKey(sum)) {
                map.put(sum, i);
            }
        }
        return result;
    }


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