Problem Description

In this problem, you are given a set of matchsticks, each with a different length. The objective is to determine whether these matchsticks can be arranged to form a perfect square without breaking any of them. This means that each matchstick must be used exactly once and all must contribute to the shape of the square.

To imagine this more concretely, think of it as a puzzle where we must create four equal-length sides (as only a square has equal sides) with the given set of matchsticks. If we can successfully do this, the function should return true, otherwise, it should return false.

Flowchart Walkthrough

Let's determine the appropriate solving technique for Leetcode 473. Matchsticks to Square using the Flowchart. We'll proceed step-by-step:

Is it a graph?

• No: The problem does not describe relationships or connections typical of graph problems.

Need to solve for kth smallest/largest?

• No: The challenge does not involve finding a specific ordinal element within a set.

Involves Linked Lists?

• No: This problem involves an array of integers (matchsticks), not linked lists.

Does the problem have small constraints?

• Yes: The problem can work within reasonably small constraints since it involves a limited collection of matchsticks, allowing feasible exhaustive searches.

Brute force / Backtracking?

• Yes: Backtracking is suitable for exploring combinations of matchsticks to meet specific conditions (e.g., forming sides of a square).

Conclusion: Following the analysis via the flowchart, we confirm that using a backtracking approach is appropriate for trying different combinations of matchsticks to form a square.

Intuition

The solution to this problem relies on a depth-first search (DFS) algorithm, which attempts to build the square by adding matchsticks one by one to each of the four sides until all matchsticks have been placed. The key insights behind this approach include:

1. The Total Length of Matchsticks: Before attempting to place matchsticks, we calculate the total length of all matchsticks by summing them. For a square to be possible, this total must be divisible by 4 equally (since a square has four equal sides). If it is not divisible, we know right away that we can't make a square, so we return false.

2. Avoiding Useless Work: Two optimizations help us reduce the computation time. First, we sort the matchsticks in descending order, beginning with the longest. This helps because if the longest matchstick is longer than the side of the square, we know it's impossible to form a square and can return false immediately. Second, when trying to place a matchstick on a side that is equal in length to a side we just attempted, we skip that attempt since the order of matchsticks on the sides doesn't matter.

3. Recursive DFS: Using a recursive function dfs(u), we try to place the uth matchstick on each side. We only proceed with the recursion if adding the matchstick to the current side does not exceed the expected length of a side (which is the total length of the matchsticks divided by 4). If, at any point, a side becomes too long, we backtrack by removing the matchstick from that side (edges[i] -= matchsticks[u]) and trying other options. If we can place the last matchstick without exceeding the side length, we know a square is possible and return true.

By carefully placing each matchstick using DFS and backtracking when necessary, the algorithm systematically explores all possible placements until it either succeeds in forming a square or exhausts all options and returns false.

Learn more about Dynamic Programming, Backtracking and Bitmask patterns.

Solution Approach

The implementation of this solution is a classic example of utilizing Depth-First Search (DFS) alongside backtracking to explore all possible combinations of placing matchsticks on four sides of a square. Here's a step-by-step explanation of the different parts of the code:

1. Preparation Step: We begin by adding up the total length of all matchsticks and trying to divide it by 4 to get the target length for each side of the square (x). If there’s a remainder (mod), or if the longest matchstick is longer than the target side length, we cannot form a square and return false.

2. Sorting: The matchsticks are sorted in descending order because placing longer matchsticks earlier can help us reach the conclusion faster. This is because if a long matchstick doesn't fit, we don't need to consider combinations of smaller ones that would also not fit.

3. Depth-First Search (DFS): The recursive function dfs(u) tries to place the uth matchstick in turn on each of the four sides. For this, we utilize an array edges of length 4, initialized with zeroes, to keep track of the current length of each side as we add matchsticks.

4. Branching and Pruning:

   • The DFS explores different branches, each representing a different combination of matchstick placements.
   • While placing matchsticks, we avoid redundant work by skipping a side if it is of the same length as the previous one (i > 0 and edges[i - 1] == edges[i]), because different permutations of sides of the same current length don't change the overall problem.
   • We ensure that adding a matchstick does not make any side longer than the target (edges[i] <= x).

5. Backtracking:

   • After trying to add a matchstick to a side, if adding the next matchstick results in exceeding the target length or if all matchsticks up to the last are placed without forming a square, the function backtracks. This means undoing the last matchstick addition (edges[i] -= matchsticks[u]) and trying a different placement.

6. Completion: If all matchsticks are placed, dfs(u) will eventually reach past the last matchstick indicating that all matchsticks have been used to form a square, and will return true.

The entry point is calling dfs(0), indicating we start with the first matchstick. The process will recurse, branching, and backtracking until a solution is found or all possibilities are exhausted. The final outcome will be the return of either true or false from the makesquare function indicating the possibility of forming a square with the given matchsticks.

Example Walkthrough

To illustrate the solution approach, let's use a small example. Suppose we have the set of matchsticks with lengths [3, 3, 3, 3, 4].

1. Preparation Step: We add up the lengths of all matchsticks. Here, the sum is 3+3+3+3+4=16. If we divide 16 by 4, the target length for each side of the square (x) is 4, and since there's no remainder, we can proceed. The longest matchstick is exactly 4 units long, which matches our target side length, so it's possible to form a square.

2. Sorting: We sort the matchsticks in descending order to get [4, 3, 3, 3, 3].

3. Depth-First Search (DFS): The recursive function dfs(u) will try to add the uth matchstick to one of the sides.

4. Branching and Pruning: We start with dfs(0) and attempt to place the matchstick of length 4 on a side. Since the array edges starts as [0, 0, 0, 0], after placing the first matchstick, it becomes [4, 0, 0, 0].

   • Next, we call dfs(1) to place the matchstick of length 3. As per our pruning rule, we start with edges[1] because edges[0] is already 4, which is the target length for a side. Now edges becomes [4, 3, 0, 0].

   • Then dfs(2) is called, and we place another 3 length matchstick on edges[2]. edges now is [4, 3, 3, 0].

   • With dfs(3), we can't place the next 3 on edges[1] because it would exceed our target. We put it in edges[2] to get [4, 3, 3, 3]. However, this is a side with the same length as the previous side (edges[1]), so we should skip this step and place it in edges[3] instead, resulting in [4, 3, 3, 3].

   • Finally, we have the last matchstick with dfs(4). We can't add it to edges[1], edges[2], or edges[3] as they're already at 3, but we can add it to edges[1] to fill the side up to the target of 4, resulting in [4, 4, 3, 3].

5. Backtracking: If at any point a move had been invalid, we would have removed the last matchstick tried, and attempted another configuration. However, in this example, all moves were valid.

6. Completion: All matchsticks have been placed and the edges array reads [4, 4, 3, 3]. The target length for each side was 4, and dfs(4) will exit successfully, showing that these matchsticks can indeed form a perfect square, returning true.

As such, we can conclude that [3, 3, 3, 3, 4] can form a square based on the described DFS and backtracking solution approach.

Solution Implementation

Time and Space Complexity

The provided code aims to determine if an array of integers (matchsticks) can be used to form a square. The core of this approach is to use Depth-First Search (DFS) to try to place each matchstick on one of the four sides of the square until all matchsticks are placed or no valid solution can be found.

Time Complexity:

The time complexity of the given algorithm is primarily dependent on the DFS search. In the worst-case scenario, DFS would explore all possible placements of matchsticks across the four sides. This exponential behavior essentially looks at 4^n combinations, where n is the number of matchsticks. However, some optimizations are present:

1. The matchsticks are sorted in descending order, which can lead to earlier termination in certain branches of the search tree when the matchstick is too long to fit on any side.
2. The algorithm also skips over sides with the same current length to avoid redundant placements.

Considering these optimizations, the upper bound on the time complexity still remains exponential, close to O(4^n), due to the nature of the problem which is essentially a NP-Complete problem akin to the Subset Sum or Partition problem. However, the actual performance can be somewhat better than O(4^n) on average due to the early pruning of the search space.

Space Complexity:

The space complexity of the algorithm is mainly due to the recursive call stack of the DFS function and the storage of the sides of the square. In the worst-case scenario, DFS recursion could go as deep as n levels where n is the number of matchsticks. Each level of recursion uses additional space for the execution context, leading to a space complexity of O(n). The edges array contains 4 integers, so it uses O(1) space.

Combining both factors, the overall space complexity is O(n), mainly due to the call stack depth in the recursive DFS search.

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