Problem Description

In this LeetCode problem, inspired by a quest in the video game Fallout 4, you are given two strings: one represents a dial (ring) and the other represents a keyword (key). The ring represents a circular dial with characters engraved on its outer edge. The initial position of the dial is such that the first character starts at the 12:00 position. Your task is to rotate the dial in either a clockwise or anticlockwise direction to spell the keyword by aligning each character of the keyword at the 12:00 position and then pressing a button to confirm each character. The goal is to find the minimum number of steps required to complete this task, where each rotation (either clockwise or anticlockwise) of one place and each press of the button count as a separate step.

When rotating the ring, you may take as many steps as necessary to bring a character to the 12:00 position, and you can choose the direction that minimizes the number of steps. Each alignment followed by a press on the center button to confirm counts as a sequence necessary to spell one character of the keyword. After spelling all characters in the key, the process completes.

Flowchart Walkthrough

Let's analyze the problem on LeetCode 514, Freedom Trail, using the Flowchart. Here is how we step through the decision-making process:

Is it a graph?

• Yes: The ring of letters can be treated as a graph where each position is a node, and the edges represent moves from one position to another.

Is it a tree?

• No: The ring represents a circular structure where you can move back and forth, so there are cyclic dependencies.

Is the problem related to directed acyclic graphs (DAGs)?

• No: The problem involves navigating a circular ring, which inherently has cycles and is not acyclic.

Is the problem related to shortest paths?

• No: Although it seems related to finding a path, it’s more about finding a sequence with minimum moves, rather than a shortest path in the traditional sense of graphs.

Does the problem involve connectivity?

• No: The problem is not about finding connected components or checking if parts of the graph are connected.

Does the problem have small constraints?

• Yes: The key in this problem is individual elements and their permutations or rotations, indicating a feasible approach using more exhaustive techniques.

Conclusion: According to the flowchart, if the problem involves small constraints, which can handle all permutations or arrangements exhaustively, using DFS/backtracking should be appropriate. Thus, the Depth-First Search pattern, particularly with backtracking, suits solving the Freedom Trail problem effectively.

Intuition

The intuition behind the solution is to use dynamic programming to keep track of the minimum steps required to align each character of the key at the 12:00 position by the time we reach that character in the sequence. Our state will depend on which character from the key we're currently looking to match and which index in the ring is currently at the top (at the 12:00 position).

To implement this, we initialize a 2D array where each element f[i][j] represents the minimum number of steps taken to spell the first i+1 characters of the key with the j-th character of ring aligned at the top.

For the base case, we look at the first character in key and compute the minimum steps necessary to get each occurrence of this character to the 12:00 position. This step accounts for both directions of possible rotation.

For each subsequent character in key, we consider each position in ring that matches the current character we would like to align and calculate the minimum steps required to reach that position from every position that matches the previous character (which we already stored in our DP array). We account for the minimum path by considering the direct distance and the wrap-around distance (the ring is circular).

The number of steps includes the actual rotations plus one step to press the button once the correct character is aligned. We repeat this process for every character in the key.

Finally, the answer is the minimum value in the last row of our DP array since it represents the minimum steps required to align the last character of the key.

Learn more about Depth-First Search, Breadth-First Search and Dynamic Programming patterns.

Solution Approach

The solution involves using dynamic programming (DP), a common technique for solving optimization problems where the solution can be built up by combining solutions to smaller subproblems.

1. Data Structures: We use a 2D array f for our DP table to keep track of the minimum steps needed, and a dictionary pos to store the indices of each character as it appears in the ring.

2. Initializing the DP Table: The DP table f is initialized with inf (infinity) to indicate that we have not yet determined the minimum steps for those positions. We have m rows in this table for each character in the key, and n columns for each character in the ring.

3. Base Case: We populate the first row of the DP table by calculating the minimum steps required to rotate each occurrence of the first character of the key to the 12:00 direction. This takes into account the minimum of a clockwise or anticlockwise rotation, which is either j steps (clockwise) or n - j steps (anticlockwise) where j is the index of the current character in the ring. We add 1 for pressing the button.

4. DP Iteration: For each subsequent character in the key, we iterate through DP states from the second character to the last character. For each index j corresponding to the current character in key and each index k corresponding to the previous character, we update f[i][j], which represents the minimum steps to reach the j-th position of the ring to spell the i-th character. We minimize over two possible distances: the direct step count abs(j - k) and the wrap-around step count n - abs(j - k). The update equation is:

   f[i][j] = min(f[i][j], f[i - 1][k] + min(abs(j - k), n - abs(j - k)) + 1)

5. Final Answer: After filling in the DP table, the final answer is the minimum number of steps among all the positions that can be used to spell the last character of the key, which is:

   min(f[-1][j] for j in pos[key[-1]])

   This represents taking the minimum steps across all possible indices where the last character of the key can be aligned.

The solution leverages the circular nature of the problem by using modular arithmetic and DP's overlapping subproblems property by storing interim solutions to avoid redundant calculations. By populating the DP table iteratively, an optimal solution to the full problem is efficiently built up from the solutions to smaller subproblems.

Example Walkthrough

Let's consider a small example to illustrate the solution approach. Suppose we have the following ring and key:

ring = "abcde"
key = "dae"

In this scenario, our goal is to align each character of key at the 12:00 position starting with 'd', followed by 'a', and finally 'e', while counting the minimum number of steps required to do so.

Step-by-Step Walkthrough:

1. Initialization

   • The ring "abcde" has 5 characters, and the key "dae" has 3 characters.
   • We create a 2D array f with dimensions 3x5, initialized with infinity, since our key has 3 characters and our ring has 5 characters.
   • We create a dictionary pos to map each character in the ring to the indices where they appear. For this example:

     pos = {'a': [0], 'b': [1], 'c': [2], 'd': [3], 'e': [4]}

2. Base Case

   • We fill in the first row of f based on the first character of key, which is 'd'.
   • The character 'd' is at the index 3 in ring. To align 'd' at the top, you can rotate clockwise 3 steps, or anticlockwise 2 steps (since the ring is circular).
   • Since the anticlockwise rotation is shorter, we choose that path and set f[0][3] to 2 (for the rotations) + 1 (for pressing the button), thus f[0][3] = 3.

3. DP Iteration

   • Now, we consider the second character in key, which is 'a'.
   • The character 'a' is at the index 0 in ring, and we can reach it from the position of 'd' (index 3).
   • We must consider both paths: rotating clockwise 3 steps or anticlockwise 2 steps from 'd' to 'a'.
   • Since we're already at 'd', the distance is 0 in this case, so we update f[1][0] with the value from f[0][3] (which is 3) + 0 (for the rotation) + 1 (for pressing the button), thus f[1][0] = 4.

4. Continuing with DP Iteration

   • For the last character 'e' in key, positioned at index 4 in ring, we consider the path from 'a' at index 0.
   • We can rotate 4 steps clockwise or 1 step anticlockwise.
   • The anticlockwise path is shorter, so we update f[2][4] with f[1][0] + 1 (for the rotation) + 1 (for pressing the button), thus f[2][4] = 6.

5. Final Answer

   • The last row of array f now contains [inf, inf, inf, inf, 6], which represents the minimum steps required to align each position of the ring to spell the last character of key.
   • The final answer is the minimum of these values, which is 6, at index 4.

Using the above steps, we navigate the dial optimally to confirm the keyword "dae" with the minimum number of steps, 6 in this case.

Solution Implementation

Time and Space Complexity

Time Complexity

The time complexity of this dynamic programming solution can be analyzed based on the nested loops present in the algorithm:

1. The first loop is iterating over the characters in the key string, which has a length of m. Therefore, it contributes O(m) to the time complexity.

2. The second and third loops are iterating over the positions of characters in the ring that match the current and previous characters of the key. In the worst case, it's possible that each character in the ring matches the key characters, hence both these loops could iterate up to n times, where n is the length of the ring.

3. The innermost statement that executes within the nested loops does constant work (calculating minimums and arithmetic operations), therefore each execution contributes O(1).

By multiplying these together, the overall worst-case time complexity is O(m * n^2).

Space Complexity

The space complexity is determined by the space required to store the dynamic programming table f and the position map pos:

1. The DP table f is an m by n matrix where m is the length of the key and n is the length of the ring. Thus, the space complexity contribution for the dynamic programming table is O(m * n).

2. The pos dictionary can hold at most n positions for each unique character in the ring. In the worst case, where all characters are unique, it could store n positions for n different characters. Thus, its contribution is O(n^2) in this scenario.

However, since the ring consists of characters, and even considering an extended character set, the number of unique characters would not realistically scale with n. Therefore, many consider the space complexity for the pos dictionary to be O(n) because the number of unique keys (characters) in the dictionary is a constant factor not dependent on the input size.

Taking the larger space complexity between these two, the overall space complexity is O(m * n).

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