Problem Description

In this problem, we are given a set of candidate numbers (an array candidates) and a target number (target). Our goal is to find all unique combinations where the sum of the numbers in each combination equals the target. Importantly, we can only use each number in candidates once in any given combination.

Another important requirement is that the solution set must not include any duplicate combinations. Even though the same number can occur multiple times in the candidates, it can only appear once in each combination.

Essentially, we need to explore all possible combinations of the numbers that add up to the target and return a list of all combinations that meet the criteria without any repetitions.

Flowchart Walkthrough

To analyze the problem using the Flowchart, let’s proceed with a step-by-step walkthrough to determine the suitable algorithm pattern:

Is it a graph?

• No: The problem does not deal with nodes and edges in the context of graph theory.

Need to solve for kth smallest/largest?

• No: The task is not about finding kth smallest or largest element.

Involves Linked Lists?

• No: The problem does not handle operations on linked lists.

Does the problem have small constraints?

• Yes: The size constraints allow exhaustive searching through the candidate numbers, fitting for a backtracking approach.

Brute force / Backtracking?

• Yes: Given the structure of the problem and the feasibility of exploring all combinations due to relatively small constraints, a backtracking approach is appropriate.

Conclusion: According to the flowchart, the suitable algorithm pattern for Leetcode 40. Combination Sum II is backtracking, as it necessitates exploring combinations that meet specific criteria, achievable within small constraints.

This systematic approach via the flowchart leads us to pinpoint the backtracking as the appropriate technique for this problem.

Intuition

The intuition behind the solution involves a backtracking technique, which is similar to depth-first search (DFS).

Since we need to find all combinations that add up to the target and any number in the candidates can be used only once, we sort the candidates first. Sorting the array helps us to easily skip over duplicate elements and avoid generating duplicate combinations.

Then, we perform DFS with backtracking, starting from the beginning of the candidates array and exploring each possibility by either including or excluding a candidate. After including a candidate in the combination, we call the function recursively, reducing the target by the value of that candidate and moving to the next index.

While exploring, we keep track of the current sum of the combination, and when the sum equals the target, we add the current combination to the answer. If at any point, the sum exceeds target or we reach the end of the candidates array, we backtrack.

To prevent duplicates, we skip subsequent candidates that are the same as the previous one at each stage in the loop. This way, we ensure that if a number has been used in a combination, the same number is not used immediately again to form another similar combination.

The result of the function will be a list of lists, where each inner list is a unique combination of numbers that sum to the target value.

Learn more about Backtracking patterns.

Solution Approach

The solution uses depth-first search (DFS), backtracking, and sorting to effectively find all unique combinations. Let's break down how each part of the code contributes to the solution:

1. Sorting the Candidates: The candidates array is first sorted to ensure that we can easily skip duplicates.

   candidates.sort()

2. Depth-First Search (DFS) Function: The dfs function is defined to handle the recursion, taking two parameters: i (the current index in the candidates list) and s (the remaining sum needed to reach the target).

   def dfs(i: int, s: int):

3. Condition to Add to the Answer: Inside the dfs function, we check if the remaining sum s is zero, meaning we found a valid combination that sums to the target. In that case, we add a copy of the current combination to the answer list.

   if s == 0:
       ans.append(t[:])

4. Base Cases for Termination: We return if the index i moves beyond the length of the candidates or if the remaining sum s is less than the current candidate, which means we can't reach the desired total with the current and subsequent candidates since they are all larger.

   if i >= len(candidates) or s < candidates[i]:
       return

5. Loop Over the Candidates: Starting from the current index to the end of candidates, we try to include the candidate in the combination:

   for j in range(i, len(candidates)):

6. Skipping Duplicates: Before including a candidate in the combination, we skip over it if it's the same as its predecessor to avoid duplicates in our answer list (since we’ve already considered this value in the previous steps).

   if j > i and candidates[j] == candidates[j - 1]:
       continue

7. Backtracking: After including a candidate, the dfs function is called recursively with the updated index (j+1) and the updated remaining sum (s - candidates[j]). After this recursive call, we backtrack by removing the last candidate from the combination and moving on to the next candidate.

   t.append(candidates[j])
   dfs(j + 1, s - candidates[j])
   t.pop()

The solution utilizes a list ans to store all the unique combinations and a temporary list t to store the current combination being constructed.

After defining dfs, the solution begins the search with:

ans = []
t = []
dfs(0, target)

The DFS and backtracking continue until all possible combinations that meet the criteria have been explored, after which ans is returned, containing all the valid combinations.

This approach efficiently explores all possible combinations and prunes the search space to avoid duplicates and unnecessary searches, thereby finding the solution set in an optimal manner.

Example Walkthrough

Let's assume our candidates array is [2, 5, 2, 1, 2], and our target is 5.

Following the solution approach:

1. First, we sort the candidates to [1, 2, 2, 2, 5].
2. The dfs function starts with i as 0 (the first index) and the target sum s as 5.
3. We enter a loop that will iterate through the candidates starting from index 0.
4. On the first iteration, j is 0, and the candidate is 1.
   • We include 1 in our temporary combination t and call dfs(1, 4), which moves to the next index and decrements the target by the included candidate’s value (5 - 1 = 4).
5. The function dfs(1, 4) starts and enters a loop starting at index 1; the first candidate it considers is 2.
   • We include 2 to make our combination [1, 2] and call dfs(2, 2).
6. The function dfs(2, 2) again finds the candidate 2 at index 2.
   • However, this time j is not greater than i, which means it's the first occurrence of this candidate in the loop, so we include 2 to get [1, 2, 2] and call dfs(3, 0).
7. Since the remaning sum s is 0 (dfs(3, 0)), we've found a valid combination which sums to the target. So we add [1, 2, 2] to our answer list ans.
8. Now, we backtrack and the temporary combination t becomes [1, 2] again.
   • The loop in dfs(2, 2) continues with the next j as 3, but the candidate 2 at index 3 is a duplicate. Thus, we skip it and continue to j as 4. Since candidates[4] is 5, which is greater than our remaining sum 2, this does not lead to a valid combination. So, we finish dfs(2, 2) without further action.
9. We continue backtracking to dfs(1, 4); t is now [1].
   • The loop resumes with the next j, which is 2. But since candidates[2] is a duplicate of the previous candidate, we skip it and do the same for j as 3.
   • For j as 4, the candidate is 5, but adding 5 to [1] will exceed the remaining sum (4). So no further action is taken.
10. Finally, we backtrack to the very first dfs(0, 5), t is now [], and we continue to the next candidate, which is 2 at j as 1.

• However, this 2 is not considered as it is a duplicate of the previous candidate.

11. The next non-duplicate candidate is the 5 at j as 4.

• We include this 5 and call dfs(5, 0).
• Since the remaining sum s is 0, we've found another valid combination that sums to the target, so [5] is added to our answer list ans.

Finally, our dfs is done exploring all possibilities, and we have ans as [[1, 2, 2], [5]], which are the unique combinations that sum to the target 5.

Solution Implementation

Time and Space Complexity

The provided Python code solves the combination sum problem where each number in the array candidates can only be used once to find all unique combinations that sum up to a given target.

Time Complexity

The time complexity of this code primarily depends on the depth of the recursion and the number of recursive calls made at each level.

1. The recursion depth is at most the target value if we choose candidates with a value of 1 every time. However, since the same candidate cannot be used repeatedly, the recursion depth is constrained by the number of candidates in the worst case.

2. At every level of recursion, we iterate over the remaining candidates, so, in the worst case, the number of recursive calls can be exponential in nature, implied by O(2^n), where n is the number of candidates. However, since we skip duplicates after sorting, the number of branches in the recursion tree can be less than that.

A more accurate bound is not straightforward because it depends on the candidates and the target value. The worst-case time complexity, without considering duplicates, is O(2^n), where n is the number of candidates.

3. The sorting operation at the start of the code takes O(n*log(n)) time.

Combining the sorting with the recursion leads to a worst-case time complexity of O(n*log(n) + 2^n). Considering that the exponential part is more dominant, we can approximate it as O(2^n).

Space Complexity

The space complexity of the code consists of:

1. Space used by the recursion stack, which in the worst case is equivalent to the depth of recursion, at most O(n) if all candidates are used.

2. Space for the temporary list t, which stores the current combination, will at most contain n values, adding another O(n).

3. Finally, the output list ans that could potentially hold all unique combinations of candidates. In the worst case, this could be all possible combinations which can be exponential, represented as O(2^n).

Hence, the overall space complexity, considering the output space and the recursion stack depth, is O(n + 2^n). Typically, the output space can be a separate consideration, and if we exclude it, the space complexity for computation is O(n). However, if we include the space needed for the output, it would be O(2^n) due to the possibility of storing all combinations.

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