Problem Description

We need to find all "stepping numbers" between two integers low and high, inclusive. A stepping number is defined as a number in which each digit is one more or one less than its neighboring digits. For example, 123 and 987 are stepping numbers, but 135 and 975 are not. The task is to produce a list of these particular numbers in sorted order without skipping any in the given range.

Flowchart Walkthrough

To determine the most suitable algorithm for solving Leetcode 1215, "Stepping Numbers," let's traverse the algorithm flowchart (the Flowchart):

Is it a graph?

• Yes: Although Stepping Numbers aren't in a traditional graph form, the challenge can be framed as a graph problem where each node represents a number, and the edges connect to numbers that can be reached by a step of +/- 1 on the last digit (like 10 to 11 or 10 to 20).

Is it a tree?

• No: The relationship between numbers is not hierarchical; a number might have connections that don't form a single parent-child path.

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

• No: The problem is to find all stepping numbers in a range, which doesn't concern directed acyclic characteristics.

Is the problem related to shortest paths?

• No: The main goal is not finding the shortest path but to enumerate all valid stepping numbers within a provided range.

Does the problem involve connectivity?

• Yes: The problem is to find all reachable numbers starting from any number within the range, where connectivity follows the stepping rule.

Does the problem have small constraints?

• No: The constraints could be the large range from 0 to 2^31 - 1. Assuming reasonable limits (as typically found in competitive programming), BFS remains efficient for graph traversal especially when exploring nodes level by level like here.

Conclusion: Following the flowchart path for a connectivity problem involving a graph that is not a tree, DAG, or about the shortest path, it suggests using BFS (Breadth-First Search) to explore all possible stepping numbers efficiently by generating each number's "neighbors" and exploring further from there.

Intuition

The concept is similar to a breadth-first search (BFS) on the numbers, but with a unique condition that defines our graph's edges: a number can only connect to another if they differ by one at the last digit. Since single-digit numbers naturally fit the stepping number definition (except for 0 which has only one neighbor), we use them as the starting points for our BFS queue, except for 0 which we include directly into the results if it's within the given range.

From each single-digit number 1 to 9, we construct new numbers by appending a digit either one less or one more than the last digit (if possible), ensuring this new number is still a stepping number. We continue this process in a BFS manner, checking at each step if our current number falls within the desired range low to high. If it does, we include it in the results. The stopping condition for our BFS is when a number exceeds high, ensuring we are not wasting resources by continuing the search beyond the given range.

By using BFS, we also take advantage of the fact that the queue keeps numbers approximately in the order of their magnitude, helping to fulfill the requirement that the list we return is sorted.

Learn more about Breadth-First Search and Backtracking patterns.

Solution Approach

The solution approach involves implementing a Breadth-First Search (BFS) algorithm. BFS is typically used to traverse or search tree or graph data structures. It starts at some arbitrary node of a graph (or root of a tree) and explores the neighbor nodes first, before moving to the next level neighbors. Here's how the BFS is used to find stepping numbers:

• First, we check if 0 is within the inclusive range given by low and high. If it is, we add 0 to the answer list because 0 is considered a stepping number.

• Then, we initialize a queue q and add the digits 1 to 9 to it. These are our starting points for generating stepping numbers since each single digit is trivially a stepping number.

• Now we follow the BFS pattern: We continuously process items from the queue until the queue is empty or we've exceeded our high limit. For each element v that we pop from the queue, we do the following:

  • Check if v is greater than high. If it is, we break from the loop as further numbers will only be larger and outside our range.

  • If v is within the range [low, high], we add v to our list of answers, ans.

  • Next, we need to generate the potential stepping numbers that can be formed using v as the base. To do this, we consider the last digit x of v.

  • If x > 0, i.e., it's possible to subtract one from it without getting a negative digit, we generate a new number by appending x - 1 to v, and we add v * 10 + x - 1 to the queue.

  • If x < 9, i.e., we can add one to the digit without exceeding 9, we create another number by appending x + 1 to v, and we add v * 10 + x + 1 to the queue.

• This process continues, growing numbers at the queue's front by one digit at a time, always checking that they are stepping numbers.

• Finally, when the queue is empty, or all remaining numbers in the queue are greater than high, the BFS search is complete. The ans list, which has been constructed in ascending order due to the nature of BFS, contains all the stepping numbers in the range [low, high].

This algorithm employs BFS effectively to navigate the space of numbers, efficiently filtering and constructing stepping numbers. It uses the queue q to keep track of candidates for stepping numbers and a list ans to store the final results in sorted order.

Example Walkthrough

Let's take a small range to illustrate the solution approach. Assume our low is 10 and high is 21. Here is how the algorithm would execute to find stepping numbers in this range:

1. Initialize the result list ans and check if 0 is within the range 10 to 21. It is not, so we do not add 0 to ans.

2. Initialize the queue q and add the digits 1 to 9 to it, because these are already stepping numbers.

3. Start the BFS by dequeuing the front of q and processing it. The process starts with 1.

4. Since 1 is less than low, it is not added to the ans, but we will use it to generate potential stepping numbers. We check for the last digit x of 1, which is 1. We can subtract 1 to get 0 and add 1 to get 2. We generate 10 and 12 and add them to the queue.

5. Continue the BFS. The next number in the queue would be 2, and we will follow the same steps to generate 21 and 23 and add to the queue. Since 21 is within the range [10, 21], it is added to ans.

6. The same process continues for queue elements 3 through 9 but all the numbers each would generate (30 and 32 for 3, 43 and 45 for 4, and so on) would be greater than the high of 21, so they do not contribute to the ans list.

7. At this stage our BFS is mostly generating numbers that are higher than 21. Once the number at the front of the queue is 22 or higher, the BFS stops processing new numbers.

8. The final ans list contains the stepping numbers in the given range: [10, 12, 21].

The algorithm uses the BFS to systematically explore larger and larger stepping numbers starting from the smallest possible ones (the single-digit numbers) while checking against the low and high constraints to build a sorted result list.

Solution Implementation

Time and Space Complexity

The time complexity of the algorithm is O(10 * 2^log(M)), where M is the highest number you have in the range. Since the algorithm only ever has numbers with up to log(M) digits in the queue and for each such number the algorithm generates at most two more numbers, it ends up with a factor of 2^log(M) operations. Multiplying by the initial range of numbers 1-9 gives us the 10 factor.

The space complexity is O(2^log(M)) because the queue can grow up to include all stepping numbers less than high. The maximum number of elements that the queue can hold is determined by the number of stepping numbers with log(M) digits, which is the number of digits in high. The stepping number sequence grows exponentially as more digits are added to the numbers, and thus the space required by the queue grows exponentially with the number of digits in high, resulting in a space complexity of O(2^log(M)).

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