1871. Jump Game VII

MediumStringDynamic ProgrammingPrefix SumSliding Window

Problem Description

You are given a binary string s that is 0-indexed, which means indexing starts from 0. Your task is to determine if you can move from the start position, index 0, to the last index of the binary string, s.length - 1, using a set of movement rules defined by two integers minJump and maxJump. The conditions for moving from index i to index j are as follows:

You can only jump to index j from index i if i + minJump <= j <= min(i + maxJump, s.length - 1).
The destination index j must have a value of '0' in the binary string (s[j] == '0').

You can only start your movement from index 0 if its value is '0', and you must end at the last index s.length - 1. The goal is to return true if reaching the last index is possible under the given conditions, otherwise return false.

Intuition

The essence of this problem is to find if there's a path from the beginning to the end of s while obeying the jump constraints. One intuitive approach to solve this problem will be using dynamic programming, where we calculate the possibility of reaching a particular index based on the possibilities of reaching previous indices.

The solution hinges on the following intuitions:

We should track reachability from the start to each index with a boolean array dp where each index i of dp signifies whether it is possible to reach s[i].
To efficiently determine if we can reach index i, we should use a prefix sum array pre_sum to quickly calculate the number of reachable indices in the range [i - maxJump, i - minJump] without iterating through all of them every time.
If any index j within the range [i - maxJump, i - minJump] is reachable (dp[j] == true) and s[i] == '0', then index i is also reachable.

Thus, starting from index 0, we iteratively compute dp and pre_sum until we reach the end of the array or until it's clear that the end is not reachable. The last element of the dp array gives us the answer to whether the end is reachable.

Learn more about Dynamic Programming, Prefix Sum and Sliding Window patterns.

Solution Approach

To implement the solution, we use dynamic programming combined with a prefix sum strategy. Here's a step-by-step breakdown of how the solution is composed:

First, initialize two arrays dp and pre_sum with lengths equal to n and n + 1 respectively, where n is the length of the binary string s. The array dp will store boolean values indicating whether each index i is reachable from the start while pre_sum stores the prefix sums of dp. Set dp[0] to True because we start from index 0 and it's already reachable by definition. Similarly, set pre_sum[1] to 1 as we can reach the first element.
Iterate through each character in the binary string starting from index 1 as we have already initialized index 0. On each iteration, check if the current character s[i] is '0'. If it's not, we can't possibly jump to this index anyway, so we move on.
If s[i] is '0', check if there's any reachable index within the window defined by the current index i minus minJump and maxJump. To do this quickly, we calculated the prefix sum up to index i - minJump and subtract it from the prefix sum up to index i - maxJump. If the resulting value is greater than 0, it means there is at least one reachable '0' we can jump from within the bounds. In this case, set dp[i] to True.
Update the prefix sum array pre_sum accordingly by adding the value at dp[i] to the prefix sum up to index i.
Continue this process until you've gone through all characters of the binary string. The last element of the dp array, dp[n - 1], gives us the final answer. If it's True, that means the end of the string is reachable; otherwise, it is unreachable.

The key algorithms and data structures used in this approach are:

Dynamic Programming (DP): Used to store the reachability state of each index as we progress through the string.
Prefix Sum Array: Allows us to efficiently calculate the sum of elements in a range, which helps in quickly determining if a jump is possible without checking every individual index within the range each time.

By using the DP and Prefix Sum Array, we efficiently solve the problem by reusing the results of the sub-problems and avoiding redundant calculations. This way, each i is checked only once, making the solution optimal in terms of time complexity.

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Example Walkthrough

Let's consider a small example to illustrate the solution. Suppose we have a binary string s = "0010110" and the given jump rules are minJump = 2 and maxJump = 3.

Step 0: Initialization

dp = [false, false, false, false, false, false, false]
pre_sum = [0, 0, 0, 0, 0, 0, 0, 0]
We can start from index 0 since s[0] == '0', so dp[0] = true and pre_sum[1] = 1.

Step 1: Checking each index

Index 1 -> s[1] == '0', but index 1 cannot be reached directly from 0 as minJump > 1, so dp[1] is not changed.
Index 2 -> s[2] == '0' and it's within the minJump and maxJump from 0 (0 + 2 <= 2 <= 0 + 3). We find that pre_sum[2 - 1 + 1] - pre_sum[2 - 3 + 1] = pre_sum[2] - pre_sum[0] = 1 - 0 = 1 > 0, indicating that a previous index is reachable. So, dp[2] = true and pre_sum[3] = pre_sum[2] + dp[2] = 1 + 1 = 2.
Index 3 -> s[3] == '1', so it can't be reached because the position does not contain '0'.
Index 4 -> s[4] == '0'. We can jump from both indices 1 and 2 to 4. However, since dp[1] is false and dp[2] is true, we only consider the 2 index. Again, we find that pre_sum[4 - 2 + 1] - pre_sum[4 - 3 + 1] = pre_sum[3] - pre_sum[2] = 2 - 1 = 1 > 0, so dp[4] = true and pre_sum[5] = pre_sum[4] + dp[4] = 2 + 1 = 3.
Index 5 -> s[5] == '1', so it's not reachable.
Index 6 -> s[6] == '0'. It's reachable from index 3, 4 with minJump and maxJump rules. But since dp[3] is false, we only consider the 4. Checking the pre_sum, pre_sum[6 - 2 + 1] - pre_sum[6 - 3 + 1] = pre_sum[5] - pre_sum[4] = 3 - 2 = 1 > 0, so dp[6] = true and pre_sum[7] = pre_sum[6] + dp[6] = 2 + 1 = 3.

Step 2: Final Result

Checking the last element in dp array, dp[6] = true. This indicates that it's indeed possible to jump to the last index using the given rules. Thus, the function should return true for this test case.

The final dp and pre_sum look like this:

dp = [true, false, true, false, true, false, true]
pre_sum = [0, 1, 1, 2, 2, 3, 3, 3]

We've successfully found a path that allows us to travel from index 0 to index 6, thereby adhering to the solution approach and confirming the possibility to reach the end index.

Solution Implementation

1class Solution:
2    def canReach(self, arr: str, min_jump: int, max_jump: int) -> bool:
3        # Length of the input string
4        length = len(arr)
5      
6        # A dynamic programming list to keep track of the possibility to reach each index
7        can_reach = [False] * length
8      
9        # The starting point is always reachable
10        can_reach[0] = True
11      
12        # Prefix sum array to keep track of number of reachable points in the string up to index i
13        prefix_sum = [0] * (length + 1)
14
15        # Initially only the first point is reachable
16        prefix_sum[1] = 1
17      
18        # Loop through each point in the string
19        for i in range(1, length):
20            # We only need to consider '0' positions since '1' positions are not reachable
21            if arr[i] == '0':
22                # Calculate the range of jumps
23                left_bound = max(0, i - max_jump) # Lower bound for the jump
24                right_bound = i - min_jump        # Upper bound for the jump
25              
26                # If the sum of reachable points between the bounds is greater than 0, 
27                # then the current point is reachable
28                if right_bound >= left_bound and prefix_sum[right_bound + 1] - prefix_sum[left_bound] > 0:
29                    can_reach[i] = True
30          
31            # Update the prefix sum array with the reachability of the current index
32            prefix_sum[i + 1] = prefix_sum[i] + int(can_reach[i])
33      
34        # Check if the last point is reachable, return the result
35        return can_reach[length - 1]
36

1class Solution {
2    public boolean canReach(String s, int minJump, int maxJump) {
3        // Get the length of the input string.
4        int length = s.length();
5
6        // Create a dynamic programming (dp) array to hold the reachability of each position.
7        boolean[] isReachable = new boolean[length];
8
9        // Always start at position 0.
10        isReachable[0] = true;
11
12        // Use a prefix sum array to keep a running sum of reachable positions.
13        int[] prefixSum = new int[length + 1];
14        prefixSum[1] = 1;
15
16        // Iterate through the string starting at position 1 since we're always starting at position 0.
17        for (int i = 1; i < length; ++i) {
18            // Check if the current position has a '0' and therefore can be landed on.
19            if (s.charAt(i) == '0') {
20                // Calculate the maximum left (furthest back we can jump from current position).
21                int left = Math.max(0, i - maxJump);
22                // Calculate the minimum right (closest jump we can make to current position).
23                int right = i - minJump;
24              
25                // Ensure that right is not less than left and that there is at least one true
26                // within the range in the prefix sum array to jump to current position.
27                if (right >= left && prefixSum[right + 1] - prefixSum[left] > 0) {
28                    // Mark the current position as reachable.
29                    isReachable[i] = true;
30                }
31            }
32
33            // Update the prefix sum array with the sum up to the current position.
34            prefixSum[i + 1] = prefixSum[i] + (isReachable[i] ? 1 : 0);
35        }
36
37        // Return if the last position in the string is reachable.
38        return isReachable[length - 1];
39    }
40}
41

1#include <vector>
2#include <string>
3
4/**
5 * Function to determine if it's possible to reach the end of a string by jumping
6 * between the positions specified by min_jump and max_jump.
7 * Each jump can only be made if the destination is a '0' character in the string.
8 * 
9 * @param s The string representing safe ('0') and unsafe ('1') positions.
10 * @param min_jump The minimum length of a jump.
11 * @param max_jump The maximum length of a jump.
12 * @return boolean indicating whether the end of the string can be reached.
13 */
14bool canReach(const std::string& s, int min_jump, int max_jump) {
15    int n = s.length(); // Length of the string
16    std::vector<int> dp(n, 0); // DP array to store whether a position is reachable
17    std::vector<int> prefix_sum(n + 1, 0); // Array to store the prefix sum of the DP
18
19    dp[0] = 1; // Starting position is always reachable
20    prefix_sum[1] = 1; // Initialize prefix sum
21  
22    // Iterate through the string to fill the DP array with reachable positions
23    for (int i = 1; i < n; i++) {
24        // Check if the current position is '0' and can potentially be jumped to
25        if (s[i] == '0') {
26            int left_bound = std::max(0, i - max_jump); // Left boundary for the window from which we can jump
27            int right_bound = i - min_jump; // Right boundary for the window from which we can jump
28
29            // If there is at least one reachable position within the window, mark the current position as reachable
30            if (left_bound <= right_bound && prefix_sum[right_bound + 1] - prefix_sum[left_bound] > 0) {
31                dp[i] = 1;
32            }
33        }
34        // Update the prefix sum array with the current reachability status
35        prefix_sum[i + 1] = prefix_sum[i] + dp[i];
36    }
37
38    // Return if the last position is reachable
39    return dp[n - 1] == 1;
40}
41
42// Example usage:
43/*
44int main() {
45    bool result = canReach("011010", 2, 3);
46    std::cout << (result ? "true" : "false") << std::endl; // Expected output: true or false
47    return 0;
48}
49*/
50

1// Function to determine if it's possible to reach the end of a string by jumping
2// between the positions specified by minJump and maxJump.
3// Each jump can only be made if the destination is a '0' character in the string.
4/**
5 * @param s The string representing safe(0) and unsafe(1) positions.
6 * @param minJump The minimum length of a jump.
7 * @param maxJump The maximum length of a jump.
8 * @returns boolean indicating whether the end of the string can be reached.
9 */
10const canReach = (s: string, minJump: number, maxJump: number): boolean => {
11    const n: number = s.length; // Length of the string
12    let dp: number[] = new Array(n).fill(0); // DP array to store whether a position is reachable
13    let prefixSum: number[] = new Array(n + 1).fill(0); // Array to store the prefix sum of the DP
14
15    dp[0] = 1; // Starting position is always reachable
16    prefixSum[1] = 1; // Initialize prefix sum
17
18    // Iterate through the string to fill the DP array with reachable positions
19    for (let i = 1; i < n; i++) {
20        // Check if the current position is '0' and can potentially be jumped to
21        if (s.charAt(i) === '0') {
22            let leftBound = Math.max(0, i - maxJump); // Left boundary for the window from which we can jump
23            let rightBound = i - minJump; // Right boundary for the window from which we can jump
24
25            // If there is at least one reachable position within the window, mark current position as reachable
26            if (leftBound <= rightBound && prefixSum[rightBound + 1] - prefixSum[leftBound] > 0) {
27                dp[i] = 1;
28            }
29        }
30        // Update the prefix sum array with current reachability status
31        prefixSum[i + 1] = prefixSum[i] + dp[i];
32    }
33
34    // Return if the last position is reachable
35    return dp[n - 1] === 1;
36};
37
38// Example usage:
39// const result: boolean = canReach("011010", 2, 3);
40// console.log(result); // Expected output: true or false
41

Time and Space Complexity

The given Python code implements a dynamic programming solution to determine if it is possible to reach the end of a string given certain jumping rules.

Time Complexity

The time complexity of the algorithm can be analyzed based on loop operations and array manipulations:

Initialization of the dp and pre_sum arrays with False and 0 respectively, which takes O(n) time each, where n is the length of the input string.
A single for-loop on the index i ranges from 1 to n-1, resulting in O(n) iterations.
Inside the for-loop, the check pre_sum[r + 1] - pre_sum[l] > 0 is a constant time operation, O(1), as it involves accessing elements of the prefix sum array and subtraction.
The update of the pre_sum array with pre_sum[i + 1] = pre_sum[i] + dp[i] is also a constant time operation, executed once each loop iteration.

Combining these observations, the overall time complexity can be considered as O(n) because the for-loop dominates the execution time.

Space Complexity

The space complexity is determined by the space required to store the dynamic programming states and additional constructs:

The dp array that stores Boolean values representing if a position i can be reached, has a size of n, contributing O(n) to the space complexity.
The pre_sum array helps keep track of the prefix sums of dp, also requiring O(n) space.

Hence, the overall space complexity of this solution is O(n), which is a sum of space used by dp and pre_sum.

In conclusion, the given code has a time complexity of O(n) and a space complexity of O(n).

Learn more about how to find time and space complexity quickly using problem constraints.

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Question 1 out of 10

Consider the classic dynamic programming of fibonacci numbers, what is the recurrence relation?

dp[i] = dp[i - 1] + dp[i - 2]

dp[i] = dp[i - 1] + 1

dp[i] = dp[i - 1] + dp[i - 2] + dp[i - 3]

dp[i] = dp[i - 1]

I don’t know