Problem Description

The problem presents us with a task: given an array arr of integers, we are required to find the total number of distinct bitwise OR results that can be obtained from all possible non-empty contiguous subarrays of arr.

To understand the problem better, let's clarify some concepts:

• Bitwise OR (|): A binary operation that takes two bit patterns of equal length and performs the logical inclusive OR operation on each pair of corresponding bits. The result in each position is 1 if at least one of the bits is 1.

• Subarray: A sequence of elements within an array that is contiguous (elements are consecutive without gaps) and non-empty.

• Distinct: Each unique value should be counted only once in the final tally.

The problem, therefore, requires us to explore and assess every possible subarray that can be generated from the given array, perform the bitwise OR operation on its elements, and count the unique outcomes.

Intuition

The intuitive approach to solving this problem might involve a brute force strategy - calculating the bitwise OR for every subarray and then utilizing a set to count the distinct values. However, this approach would result in a high time complexity due to the multiple nested loops required (each subarray is recursively built starting from each element).

The solution code, however, uses a clever strategy that reduces the amount of redundant computation:

1. Iterative Building: It takes advantage of the fact that the bitwise OR of a subarray ending at position i depends upon the bitwise OR result of the subarray ending at i-1. The prev variable is used to accumulate the bitwise OR result till the current element, so that for the next element, we don't have to start from scratch.

2. Early Breaking: It recognizes that if a new subarray's bitwise OR equals the accumulated OR (prev), we can stop early because all subsequent subarrays will only give the same or greater OR results, which we would already have encountered due to the nature of the OR operation building on previous subarrays.

3. Set for Uniqueness: The set s is used to store unique bitwise OR results. This way, every time we add a new OR result, duplicate values are inherently avoided.

The main algorithm goes as follows:

• Initialize a set s to keep track of distinct bitwise OR results.
• Loop over each element v in the array:
  • Start a prev accumulator that holds the bitwise OR up to the current element.
  • Loop backwards from the current element to the beginning of the array:
    • Calculate the bitwise OR for the current subarray and add it to the set s.
    • Once the current subarray OR equals the accumulated prev OR, break early.
• Finally, return the length of set s, which represents the number of distinct bitwise ORs.

By using the set and early breaking, the solution immensely reduces the number of calculations compared to the brute force approach, and therefore, is much more efficient.

Learn more about Dynamic Programming patterns.

Solution Approach

The implementation of the reference solution makes use of sets and two pointers to efficiently compute the distinct bitwise ORs of all subarrays. Here's how the approach works in detail:

1. We initialize an empty set s that will hold the distinct bitwise OR results. Apart from this, we have a prev variable that is used to keep track of the bitwise OR up to the current element in the outer loop, which goes through each element i in the array.

2. As we iterate over each element v in the array using an index i, we first update the prev variable to hold the bitwise OR between itself and the current element (prev |= v). This new prev will be the bitwise OR of all elements from the beginning of the array up to the current element i.

3. In the inner loop, we start from the current element and go backwards through the array using another index j. We create a new variable curr to hold the result of bitwise ORs for the subarrays ending in the current element i.

4. During each iteration of the inner loop, we update curr to be the bitwise OR of itself and the current jth element (curr |= arr[j]).

5. After updating curr, we add it to the set s. This operation ensures that only distinct OR results are kept, as sets do not store duplicate elements.

6. The early breaking condition is checked (if curr == prev), which is based on the understanding that once the current subarray's OR matches the overall OR up to the current element (prev), all subsequent larger subarrays will yield the same OR value and have been considered before. When this condition is met, the inner loop breaks, avoiding unnecessary computation.

7. Lastly, once both loops are done, the length of set s will represent the number of distinct bitwise ORs that can be formed from all the subarrays of arr. This is the result that the function returns.

The above steps form an efficient algorithm as it reduces the number of subarrays we need to check, leveraging the properties of the bitwise OR operation and making use of sets to maintain distinct entries. Such an optimization is essential to pass all test cases on platforms like LeetCode, where the input size can be large and brute force methods would result in a timeout error.

Example Walkthrough

To illustrate the solution approach, let's take a small example:

Let's say we have an array arr = [1, 2, 3].

We will apply the solution approach step by step:

1. Initialize a set and variables:

   • Set s = {}
   • Variable prev is initialized.

2. Outer loop - Iterate over each element in the array:

   • For element 1 (i=0), set prev = 1.
   • For element 2 (i=1), prev becomes prev | 2 = 1 | 2 = 3.
   • For element 3 (i=2), prev becomes prev | 3 = 3 | 3 = 3.

3. Inner loop - Backward iteration from current element:

   • At i=0: Only one subarray [1]:
     • curr = 1
     • Set s becomes {1}
   • At i=1: Subarrays [2], [1, 2]:
     • curr = 2, add to set s -> {1, 2}
     • curr = 2 | 1 = 3, add to set s -> {1, 2, 3}
     • Since curr now equals prev, inner loop breaks early.
   • At i=2: Subarrays [3], [2, 3], but we stop at [2, 3] because:
     • curr = 3, already in set s, no need to add.
     • Loop attempts to compute curr = 3 | 2, but since prev is already 3, the inner loop breaks.

4. Early Breaking:

   • Utilized at each step in the inner loop when curr matches prev.

5. Final Result:

   • The length of set s is 3, representing the distinct bitwise OR results: {1, 2, 3}.

Each bitwise OR operation only computes new results, while duplicates are ignored due to the set data structure. By breaking early from the inner loop, we avoid unnecessary computation, optimizing the process. This compact example covers all the main elements present in the solution approach, demonstrating its efficiency and how it leads to the final answer.

Solution Implementation

Time and Space Complexity

The given code aims to find the number of distinct subarray bitwise ORs. To do this, it iterates over the given array and computes the OR of elements from the current element to all previous elements by keeping a record of the previous OR in prev and the current progression in curr.

Time Complexity

The time complexity of this algorithm mainly depends on the number of iterations within the double-loop structure.

• The outer loop runs exactly n times where n is the number of elements in arr.
• The inner loop runs up to i+1 times in the worst case (when curr never equals prev early).

However, due to the properties of the bitwise OR operation, repetitions are likely to occur much earlier, resulting in earlier breaks from the inner loop. Specifically, the sequence of ORs will eventually stablize into a set of values that does not grow with each additional OR operation. The actual number of unique elements in these OR sequences across all iterations is bounded by a factor much smaller than n^2.

While it's difficult to put a precise bound on this without specifics about the input distribution, let's denote the average unique sequence length as k (which is considerably smaller than n due to the saturation of OR operations). Therefore, the total number of operations is approximately O(n*k).

However, it is important to note that k is not guaranteed to be a constant and its relation with n can depend heavily on the input, implying that in the worst case the time complexity could tend towards O(n^2), but in practical scenarios, it is expected to perform significantly better.

Space Complexity

The space complexity is due to the set s that is used to store the unique subarray OR results.

• In the worst case, each subarray OR could be unique, which means the set could grow to the size of the sum of all subarray counts. As with the time complexity argument, this won't actually occur due to the saturation of bitwise ORs.

Let m represent the maximum possible unique OR values which can be much less than the total subarray count of roughly n*(n+1)/2. Therefore, the space complexity can be approximated as O(m).

In conclusion, the time complexity of the code is approximately O(n*k) (with k being influenced by the input nature and much smaller than n) and space complexity is around O(m) for storing the unique OR results set, where m represents the maximum number of unique OR values across all subarrays.

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