The chess knight has a unique movement, it may move two squares vertically and one square horizontally, or two squares horizontally and one square vertically (with both forming the shape of an L). The possible movements of chess knight are shown in this diagram:
A chess knight can move as indicated in the chess diagram below:
We have a chess knight and a phone pad as shown below, the knight can only stand on a numeric cell (i.e. blue cell).
Given an integer n, return how many distinct phone numbers of length n we can dial.
You are allowed to place the knight on any numeric cell initially and then you should perform n - 1 jumps to dial a number of length n. All jumps should be valid knight jumps.
As the answer may be very large, return the answer modulo 109 + 7.
Example 1:
Input: n = 1 Output: 10 Explanation: We need to dial a number of length 1, so placing the knight over any numeric cell of the 10 cells is sufficient.
Example 2:
Input: n = 2 Output: 20 Explanation: All the valid number we can dial are [04, 06, 16, 18, 27, 29, 34, 38, 40, 43, 49, 60, 61, 67, 72, 76, 81, 83, 92, 94]
Example 3:
Input: n = 3131 Output: 136006598 Explanation: Please take care of the mod.
Constraints:
1 <= n <= 5000This approach uses dynamic programming to store and reuse the number of dialable numbers of length n starting from each digit. We maintain a 2D DP table where dp[i][j] indicates how many numbers of length i can start from digit j.
The primary idea is to iterate from i=1 to n, updating the DP table based on the moves possible from each digit in the previous state.
This function uses dynamic programming to calculate the number of valid phone numbers. The moves are stored in a dictionary with each digit pointing to its possible knight moves. We initialize a 2D list dp where dp[i][j] represents the number of numbers of length i+1 starting from digit j. The result is obtained by summing up the values in the last row of the dp table.
Java
C++
Time Complexity: O(n) for each of the 10 digits, resulting in O(10n) operations.
Space Complexity: O(10n) due to the DP table.
This approach optimizes the dynamic programming technique by reducing space complexity. Instead of using a full 2D array, we use two 1D arrays to track only the current and the previous state, thus reducing the space required from O(n*10) to O(20).
This function uses a space-efficient dynamic programming approach. Instead of maintaining a 2D table, it uses two lists: prev and curr, which track the current and previous counts of numbers of length n. The prev list is updated at each step to effectively simulate the transition from i to i+1 without maintaining an entire history.
Java
C++
Time Complexity: O(n) - iterating over digits with constant time calculations;
Space Complexity: O(10) as we only hold current and previous state arrays.
| Approach | Complexity |
|---|---|
| Dynamic Programming Approach | Time Complexity: |
| Optimized Dynamic Programming with Space Reduction | Time Complexity: |
Knight Dialer - Leetcode 935 - Python • NeetCodeIO • 10,814 views views
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