LeetCode Day Dynamic Programming Part 10

300. Longest Increasing Subsequence

Given an integer array nums, return the length of the longest strictly increasing
subsequence
.

Example 1:

Input: nums = [10,9,2,5,3,7,101,18]
Output: 4
Explanation: The longest increasing subsequence is [2,3,7,101], therefore the length is 4.
Example 2:

Input: nums = [0,1,0,3,2,3]
Output: 4
Example 3:

Input: nums = [7,7,7,7,7,7,7]
Output: 1

Constraints:

1 <= nums.length <= 2500
-10^4 <= nums[i] <= 10^4

Follow up: Can you come up with an algorithm that runs in O(n log(n)) time complexity?
Original Page

Wrong Code

    public int lengthOfLIS(int[] nums) {
        int start = nums[0];
        int pre = nums[0];
        int preMax = nums[0];
        int cnt = 1;
        int max = 1;

        for(int i=1; i<nums.length; i++){
            if(nums[i] < start){
                max = Math.max(max, cnt);
                start = nums[i];
                cnt = 1;
            } 
            else if(nums[i] > pre){
                cnt ++;
            }
            pre = nums[i];
            System.out.println("cur:"+nums[i] + " pre:"+pre+ " count:" + cnt);
        }
        return Math.max(max, cnt);
    }


</p>
<h2>
  
  
  Fix bug
</h2>


<pre class="brush:php;toolbar:false">

Learn From others treemap

    public int lengthOfLIS(int[] nums) {


        TreeMap<Integer,Integer> map = new TreeMap<>();

        map.put(Integer.MIN_VALUE,0);

       for(int i: nums)
       {
           map.put(i,map.get(map.lowerKey(i))+1);
           while(map.higherKey(i)!=null && map.get(map.higherKey(i))<=map.get(i)) 
           {
            map.remove(map.higherKey(i));
           }
       }

       return map.get(map.lastKey());

    }

---

674. Longest Continuous Increasing Subsequence

Given an unsorted array of integers nums, return the length of the longest continuous increasing subsequence (i.e. subarray). The subsequence must be strictly increasing.

A continuous increasing subsequence is defined by two indices l and r (l < r) such that it is [nums[l], nums[l + 1], ..., nums[r - 1], nums[r]] and for each l <= i < r, nums[i] < nums[i + 1].

Example 1:

Input: nums = [1,3,5,4,7]
Output: 3
Explanation: The longest continuous increasing subsequence is [1,3,5] with length 3.
Even though [1,3,5,7] is an increasing subsequence, it is not continuous as elements 5 and 7 are separated by element
4.
Example 2:

Input: nums = [2,2,2,2,2]
Output: 1
Explanation: The longest continuous increasing subsequence is [2] with length 1. Note that it must be strictly
increasing.

Constraints:

1 <= nums.length <= 10^4
-10^9 <= nums[i] <= 10^9
Original Page

Greedy Algorithm

    public int findLengthOfLCIS(int[] nums) {
        if(nums.length < 1){
            return 0;
        }
        int res = 1;
        int cnt = 1;
        int pre = nums[0];
        for(int i=1; i<nums.length; i++){
            if(nums[i] > pre){
                cnt++;
            }else{
                res = Math.max(res, cnt);
                cnt = 1;
            }
            // System.out.println("cur: " + nums[i] + " pre:" + pre + " count:" + cnt);
            pre = nums[i];
        }
        return Math.max(res, cnt);
    }

Dynamic Programming

Different from the previous question, in this question we could only consider continuously increasing subsequences, which simplifies the process.

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