The factorial of a positive integer n is the product of all positive integers less than or equal to n.
factorial(10) = 10 * 9 * 8 * 7 * 6 * 5 * 4 * 3 * 2 * 1.We make a clumsy factorial using the integers in decreasing order by swapping out the multiply operations for a fixed rotation of operations with multiply '*', divide '/', add '+', and subtract '-' in this order.
clumsy(10) = 10 * 9 / 8 + 7 - 6 * 5 / 4 + 3 - 2 * 1.However, these operations are still applied using the usual order of operations of arithmetic. We do all multiplication and division steps before any addition or subtraction steps, and multiplication and division steps are processed left to right.
Additionally, the division that we use is floor division such that 10 * 9 / 8 = 90 / 8 = 11.
Given an integer n, return the clumsy factorial of n.
Example 1:
Input: n = 4 Output: 7 Explanation: 7 = 4 * 3 / 2 + 1
Example 2:
Input: n = 10 Output: 12 Explanation: 12 = 10 * 9 / 8 + 7 - 6 * 5 / 4 + 3 - 2 * 1
Constraints:
1 <= n <= 104Problem Overview: Clumsy factorial modifies the traditional factorial by applying operations in a repeating order: *, /, +, -. Starting from N, you process numbers down to 1. Multiplication and division have higher precedence than addition and subtraction, so the evaluation order matters. The challenge is simulating these operations correctly while respecting operator precedence.
Approach 1: Stack-Based Simulation (O(n) time, O(n) space)
This method directly simulates the expression evaluation using a stack. Push the first value N onto the stack, then iterate from N-1 down to 1 while cycling through the operations *, /, +, and -. For multiplication and division, pop the top element, apply the operation with the current number, and push the result back. For addition, push the number. For subtraction, push the negative value so the final sum automatically handles it. After processing all numbers, sum the stack to produce the result. This works because the stack naturally handles precedence for multiplication and division before addition and subtraction. The approach is straightforward and mirrors how expression evaluation works in many stack-based parsers.
Approach 2: Direct Arithmetic Pattern (O(n) time, O(1) space)
The sequence of operations creates a predictable pattern in the result. Instead of storing intermediate values, you can track the current term and accumulate results as you iterate from N to 1. Multiplication and division are applied immediately to the current term, while addition and subtraction finalize the previous term and start a new one. Another common optimization uses the observation that clumsy factorial results follow a repeating pattern depending on N % 4 for larger values. By leveraging this arithmetic behavior, you compute the final value with constant extra memory. This approach fits well when the goal is minimizing memory usage or when recognizing patterns in math-based problems.
Both techniques rely on straightforward iteration and controlled operator application, essentially simulating the evaluation of an arithmetic expression. The stack approach emphasizes correctness and clarity, while the arithmetic method focuses on reducing auxiliary space and exploiting patterns that emerge from the operator cycle. Problems involving expression evaluation or sequential operations often appear in simulation tasks.
Recommended for interviews: The stack-based approach is the most expected solution. It clearly demonstrates understanding of operator precedence and expression evaluation. Interviewers can easily follow the logic because every operation is simulated step by step. After presenting that solution, mentioning the O(1) arithmetic pattern optimization shows deeper insight into the mathematical structure of the problem.
This approach uses a stack data structure to store intermediate results. We iterate over the numbers from n to 1, applying the operations in the sequence *, /, +, -. At each step, we push results onto a stack and pop them for further calculations. This helps in maintaining the correct order of operations as multiplication and division have higher precedence.
The C solution uses a dynamic array as a stack to compute the clumsy factorial. We iterate over each operator, applying it to the current number and maintaining results on the stack. Finally, we sum all elements in the stack to get the result.
Time complexity: O(n), as we iterate through each number once.
Space complexity: O(n), due to the stack storing results.
This approach leverages direct arithmetic manipulation without extra data structures like a stack, utilizing the order of operations directly within a loop. This is useful when maintaining space efficiency.
The C solution computes the clumsy factorial using nested conditional arithmetic to handle cycles of operations without using extra data structures.
The computation handles blocks of four operations and accounts for dangling operators when n becomes low.
Time complexity: O(n)
Space complexity: O(1)
| Approach | Complexity |
|---|---|
| Stack-Based Approach | Time complexity: O(n), as we iterate through each number once. |
| Direct Arithmetic Approach | Time complexity: O(n) |
| Approach | Time | Space | When to Use |
|---|---|---|---|
| Stack-Based Simulation | O(n) | O(n) | Best for clarity and interviews when demonstrating operator precedence handling |
| Direct Arithmetic Pattern | O(n) | O(1) | Useful when minimizing memory usage or leveraging mathematical patterns |
1006. (Medium) Clumsy Factorial - Daily Leetcode (Day 87) • yeetcode • 1,899 views views
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