By Long Luo

This article is the solution Pattern of Data Pairs Problem: Sorting First then Greedy of Problem 406. Queue Reconstruction by Height.

Intuition

The Data Pair \(\textit{people}[h, k]\), \(h_i\) represents the i-th person of height \(h_i\), \(k_i\) represents the other people in front who have a height greater than or equal to \(h_i\).

Pattern:

When face such kind of problem with Data Pair, sorting first will simplify the problem.

Usually, we will sort the first element in ascending order, while the second element in descending order, or sort the first element in descending order with the second element in ascending order.

Greedy

We sort the \(\textit{people}\) pairs first by \(height\) in descending order, and \(k\) in ascending order.

According to the descending order of \(height\), for each person, the number of people before him is the number of people whose height is greater than or equal to him.

According to the ascending order of \(k\), because the \(k\) is increase later, which can reduce the number of insert operations.

Take the example:

After sorting:

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[7,0], [7,1], [6,1], [5,0], [5,2], [4,4]

Insert one by one:

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[7,0]
[7,0], [7,1]
[7,0], [6,1], [7,1]
[5,0], [7,0], [6,1], [7,1]
[5,0], [7,0], [5,2], [6,1], [7,1]
[5,0], [7,0], [5,2], [6,1], [4,4], [7,1]

By Long Luo

This article is the solution 6 Approaches: Cycle, API, Divide and Conquer, Low Bit, Bit Set, Recursion of Problem 191. Number of 1 Bits.

Here shows 6 Approaches to slove this problem: Cycle, Divide and Conquer, LowBit, Bit Set, Recursion.

Brute Force: Cycle

Juse use a Loop to check if each bit of the binary bits of the given integer \(n\) is \(1\).

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public int hammingWeight(int n) {
    int cnt = 0;
    for (int i = 0; i < 32 && n != 0; i++) {
        if ((n & 0x01) == 1) {
            cnt++;
        }

        n = n >>> 1;
    }

    return cnt;
}

Analysis

• Time Complexity: \(O(k)\).
• Space Complexity: \(O(1)\).

API

Use API: \(\texttt{Integer.bitCount}\).

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public static int hammingWeight(int n) {
    return Integer.bitCount(n);
}

Analysis

• Time Complexity: \(O(logk)\).
• Space Complexity: \(O(1)\).

By Long Luo

This article is the solution 3 Approaches: Sorting, Priority Queue and Divide and Conquer of Problem 215. Kth Largest Element in an Array.

Here shows 3 Approaches to slove this problem: Sorting, Priority Queue, Divide and Conquer.

Sorting

Sort the array first, then the \(k-th\) element of the array.

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public int findKthLargest(int[] nums, int k) {
    if (nums == null || nums.length == 0) {
        return 0;
    }

    int len = nums.length;
    Arrays.sort(nums);
    return nums[len - k];
}

Analysis

• Time Complexity: \(O(nlogn)\).
• Space Complexity: \(O(logn)\).

By Long Luo

This article is the solution Priority Queue + Greedy: Consider Ladder as a One-time Unlimited Bricks of Problem 1642. Furthest Building You Can Reach.

Intuition

While we are moving, we will need to use bricks or ladders several times. Suppose we have to move to the next building, which means we must use one ladder or \(\Delta h\) bricks, so there is a question, whether to use ladders or bricks?

If the gap of buildings is large, we may use the ladder, otherwise we use the bricks.

We can consider ladder as a one-time unlimited number of bricks, That is, if we have l ladders, we will use the ladder on the \(l\) times where \(\Delta h\) is the largest, and use the bricks in the rest.

Therefore, we got the answer. We maintain no more than \(l\) largest \(\Delta h\) using priority queues, and these are where the ladders are used. For the remaining \(\Delta h\), we need to use bricks, so we need to accumulate them, if the \(sum\) exceeds the number of bricks \(b\), then we have move to the furthest building.

The code is as follows:

By Long Luo

This article is the solution 2 Approaches: DFS and BFS with Detailed Explanation of Problem 199. Binary Tree Right Side View.

Here shows 2 Approaches to slove this problem: DFS and BFS.

DFS

We traverse the tree in the order of \(\textit{root node} \to \textit{right subtree} \to \textit{left subtree}\) to ensure that each level traverse the rightmost node first.

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public List<Integer> rightSideView_dfs(TreeNode root) {
    List<Integer> ans = new ArrayList<>();
    if (root == null) {
        return ans;
    }

    dfs(root, 1, ans);
    return ans;
}

public void dfs(TreeNode root, int depth, List<Integer> numList) {
    if (root == null) {
        return;
    }

    if (numList.size() < depth) {
        numList.add(root.val);
    }

    dfs(root.right, depth + 1, numList);
    dfs(root.left, depth + 1, numList);
}

Analysis

• Time Complexity: \(O(n)\)
• Space Complexity: \(O(n)\)

BFS

We can use BFS to traverse the levels of the tree and record the last element of each level.

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public List<Integer> rightSideView_bfs(TreeNode root) {
    List<Integer> ans = new ArrayList<>();
    if (root == null) {
        return ans;
    }

    Queue<TreeNode> queue = new LinkedList<>();
    queue.offer(root);
    while (!queue.isEmpty()) {
        int size = queue.size();
        for (int i = size - 1; i >= 0; i--) {
            TreeNode node = queue.poll();
            if (i == size - 1) {
                ans.add(node.val);
            }

            if (node.right != null) {
                queue.offer(node.right);
            }

            if (node.left != null) {
                queue.offer(node.left);
            }
        }
    }

    return ans;
}

Analysis

• Time Complexity: \(O(n)\).
• Space Complexity: \(O(n)\).

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