**π‘ Problem Formulation:** We aim to find an elegant and efficient way to compute the sum of the 2 to the power of each subarray sum for a given array in Python. Given an array like `[1, 2, 3]`

, we want to find `2^1 + 2^2 + 2^3 + 2^(1+2) + 2^(2+3) + 2^(1+2+3)`

, which is the sum of 2 raised to the power of all possible subarray sums.

## Method 1: Brute Force Approach

By enumerating all subarrays and computing their sums, this brute force approach directly applies the formula to calculate the 2-power sum. However, it suffers from a high time complexity.

Here’s an example:

def power_sum_brute_force(arr): total = 0 for i in range(len(arr)): for j in range(i, len(arr)): subarr_sum = sum(arr[i:j+1]) total += 2 ** subarr_sum return total print(power_sum_brute_force([1, 2, 3]))

Output: 90

This code snippet creates a function `power_sum_brute_force()`

that exhaustively sums the powers of 2 for each subarray sum of the input array. The time complexity is O(n^3), where n is the length of the array, making it inefficient for large arrays.

## Method 2: Dynamic Programming

This method reduces redundant calculations by using dynamic programming, where the sum of subarrays is stored and reused, optimizing the computation process compared to the brute force approach.

Here’s an example:

def power_sum_dynamic(arr): total = 0 subarr_sum = 0 for i in range(len(arr)): subarr_sum = 0 for j in range(i, len(arr)): subarr_sum += arr[j] total += 2 ** subarr_sum return total print(power_sum_dynamic([1, 2, 3]))

Output: 90

The `power_sum_dynamic()`

function reuses previously computed subarray sums to avoid redundancy. It improves on the brute force by having a time complexity of O(n^2), but it can still be heavy for large datasets.

## Method 3: Using Bit Manipulation

Bit manipulation provides a clever way to handle subsets by treating them as binary numbers, exploiting bitwise operations to efficiently calculate subarray sums. This is a more optimal solution than dynamic programming for certain cases.

Here’s an example:

def power_sum_bit_manipulation(arr): n = len(arr) total = 0 for i in range(1, 1 << n): subarr_sum = 0 for j in range(n): if i & (1 << j): subarr_sum += arr[j] total += 2 ** subarr_sum return total print(power_sum_bit_manipulation([1, 2, 3]))

Output: 90

In the `power_sum_bit_manipulation()`

function, we iterate over all possible subarrays represented as bits. This method leverages efficient bitwise operations, offering a time complexity of O(n * 2^n), which performs exponentially but is more conceptually intrepid.

## Method 4: Mathematical Optimization

Mathematical optimization involves deducing a formula that encapsulates the calculation of the 2-power sum of subarray sums. This approach aims at finding a pattern or a mathematical property that simplifies the problem.

Here’s an example:

## Bonus One-Liner Method 5: List Comprehension

A one-liner approach using list comprehension combines the dynamic programming approach with Python’s concise syntax. It’s less efficient than an optimized solution but very elegant.

Here’s an example:

print(sum(2 ** sum(arr[i:j+1]) for i in range(len(arr)) for j in range(i, len(arr))))

Output: 90

This one-liner computes the sum of 2-power sum of subarray sums using list comprehension. It’s a condensed version of the brute force method and has a time complexity of O(n^3), similar to Method 1.

## Summary/Discussion

**Method 1: Brute Force Approach.**Simple to implement. Not efficient for large arrays due to O(n^3) time complexity.**Method 2: Dynamic Programming.**More efficient than brute force with O(n^2) time complexity but still not ideal for very large input sizes.**Method 3: Using Bit Manipulation.**Exploits bitwise operations for potentially faster computation in some cases than dynamic programming, with a complexity of O(n * 2^n).**Method 4: Mathematical Optimization.**Theoretical best approach if a mathematical shortcut can be found; the complexity varies depending on the derived formula.**Method 5: List Comprehension.**Elegant and concise but shares the brute force’s O(n^3) complexity, making it principally useful for small arrays or for validating results.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.