You are given an integer array nums.
Start by selecting a starting position curr such that nums[curr] == 0, and choose a movement direction of either left or right.
After that, you repeat the following process:
curr is out of the range [0, n - 1], this process ends.nums[curr] == 0, move in the current direction by incrementing curr if you are moving right, or decrementing curr if you are moving left.nums[curr] > 0:
nums[curr] by 1.A selection of the initial position curr and movement direction is considered valid if every element in nums becomes 0 by the end of the process.
Return the number of possible valid selections.
Example 1:
Input: nums = [1,0,2,0,3]
Output: 2
Explanation:
The only possible valid selections are the following:
curr = 3, and a movement direction to the left.
[1,0,2,0,3] -> [1,0,2,0,3] -> [1,0,1,0,3] -> [1,0,1,0,3] -> [1,0,1,0,2] -> [1,0,1,0,2] -> [1,0,0,0,2] -> [1,0,0,0,2] -> [1,0,0,0,1] -> [1,0,0,0,1] -> [1,0,0,0,1] -> [1,0,0,0,1] -> [0,0,0,0,1] -> [0,0,0,0,1] -> [0,0,0,0,1] -> [0,0,0,0,1] -> [0,0,0,0,0].curr = 3, and a movement direction to the right.
[1,0,2,0,3] -> [1,0,2,0,3] -> [1,0,2,0,2] -> [1,0,2,0,2] -> [1,0,1,0,2] -> [1,0,1,0,2] -> [1,0,1,0,1] -> [1,0,1,0,1] -> [1,0,0,0,1] -> [1,0,0,0,1] -> [1,0,0,0,0] -> [1,0,0,0,0] -> [1,0,0,0,0] -> [1,0,0,0,0] -> [0,0,0,0,0].Example 2:
Input: nums = [2,3,4,0,4,1,0]
Output: 0
Explanation:
There are no possible valid selections.
Constraints:
1 <= nums.length <= 1000 <= nums[i] <= 100i where nums[i] == 0.When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:
Imagine this is a puzzle where your only move is to subtract one from a group of `k` neighboring numbers. The brute force approach is to try every single possible sequence of moves, like exploring every path in a maze, to see if any path leads to a state where all numbers are zero.
Here's how the algorithm would work step-by-step:
def make_array_zero_brute_force(numbers, window_size):
def explore_paths(current_numbers):
# If all numbers are zero, we have found a valid sequence of operations and exited the maze.
if all(element == 0 for element in current_numbers):
return True
# Explore every possible move from the current state by selecting a starting position for the operation.
for start_index in range(len(current_numbers) - window_size + 1):
is_possible_move = all(current_numbers[index] > 0 for index in range(start_index, start_index + window_size))
if is_possible_move:
# Apply the operation to a copy so we can backtrack to the original state if this path fails.
next_state_numbers = list(current_numbers)
for index_in_window in range(window_size):
next_state_numbers[start_index + index_in_window] -= 1
if explore_paths(next_state_numbers):
return True
return False
return explore_paths(list(numbers))The optimal strategy avoids guesswork by processing the numbers in a single pass from left to right. At each step, the required action is forced: we must perform just enough operations to zero out the current number. By tracking the ripple effect of these forced decisions, we can efficiently determine if the entire sequence can be cleared.
Here's how the algorithm would work step-by-step:
def make_array_elements_equal_to_zero(numbers_list, operation_length_k):
# This running tally tracks the sum of all subtraction waves currently affecting an element.
current_subtraction_tally = 0
for current_index in range(len(numbers_list)):
# A subtraction wave's effect is finite; we undo its contribution once we are past its end point.
if current_index >= operation_length_k:
current_subtraction_tally -= numbers_list[current_index - operation_length_k]
effective_value = numbers_list[current_index] - current_subtraction_tally
# An element's value must not become negative, as we can only ever subtract, not add.
if effective_value < 0:
return False
# This effective value is the new subtraction wave we must start, so we add it to the tally.
current_subtraction_tally += effective_value
numbers_list[current_index] = effective_value
# Any remaining tally implies operations would affect elements beyond the array, which is invalid.
return current_subtraction_tally == 0| Case | How to Handle |
|---|---|
| Input array with a single element, which must be zero e.g., [0] | The simulation correctly handles moving out of bounds immediately, resulting in a valid case as the array is already all zeros. |
| Input array where all elements are already zero | The simulation simply moves the cursor in the chosen direction until it goes out of bounds, correctly identifying any start as valid. |
| A single large non-zero element adjacent to the only starting zero, e.g., [100, 0] | The simulation correctly models the oscillation required to decrement the large number, distinguishing the valid starting direction from the invalid one. |
| Symmetrical non-zero elements around a central zero, e.g., [1, 0, 1] | A correct simulation will track the cursor as it crosses the starting zero multiple times to clear elements on both sides. |
| An unbalanced configuration where no solution exists, e.g., [2, 0, 1] | The simulation will run for all potential starts, but in each case, it will terminate with non-zero elements remaining in the array. |
| Multiple, separated zeros providing different starting points | The solution must iterate through every possible starting zero and direction, as the outcome can be different for each choice. |
| Inputs at maximum constraints (N=100, nums[i]=100) | The direct simulation approach must be efficient enough to handle the maximum number of steps, which can be up to O(sum(nums) * N). |
| A starting zero is at an edge of the array, e.g., [0, 5, 5] | Choosing to move away from the other elements will cause immediate termination, which the simulation correctly identifies as an invalid choice. |