Given a sorted array of integers nums, find the smallest index where we can place a given value such that the array remains sorted
Example 1:
Input: nums = [1, 2, 3, 5, 7], value = 4
Output: 3
Explanation: Placing the value 4 on the 4th index we obtain nums = [1, 2, 3, 4, 5, 7]
Example 2:
Input: nums = [1, 2, 3], value = 2
Output: 1
Explanation: Placing the value 2 on the 1st index we obtain nums = [1, 2, 2, 3]
Your algorithm should run in O(n) time and use O(1) extra space.
The core challenge of this problem is to find the correct position to insert a given value into a sorted array such that the array remains sorted. This is a common problem in computer science, often referred to as finding the "lower bound" of a value in a sorted array.
Common applications include binary search algorithms, insertion operations in sorted data structures, and more.
Potential pitfalls include misunderstanding the requirement to maintain the sorted order and not handling edge cases where the value is smaller than all elements or larger than all elements in the array.
To solve this problem, we can use a simple linear search approach. We iterate through the array and find the first element that is greater than or equal to the given value. This approach ensures that we find the correct position to insert the value while maintaining the sorted order.
While a binary search approach could be more efficient with a time complexity of O(log n), the problem constraints specify an O(n) solution, so we will focus on that.
The naive solution involves iterating through the array and checking each element to find the correct position. This approach is straightforward but not optimal for large arrays.
The optimized solution also involves iterating through the array, but we can stop as soon as we find the correct position, making it more efficient in practice.
Here is a step-by-step breakdown of the algorithm:
def find_insert_position(nums, value):
"""
Find the smallest index where we can place the given value such that the array remains sorted.
:param nums: List[int] - A sorted list of integers.
:param value: int - The value to be inserted.
:return: int - The index where the value should be inserted.
"""
# Iterate through the array
for i in range(len(nums)):
# Check if the current element is greater than or equal to the value
if nums[i] >= value:
return i
# If no such element is found, return the length of the array
return len(nums)
# Example usage
print(find_insert_position([1, 2, 3, 5, 7], 4)) # Output: 3
print(find_insert_position([1, 2, 3], 2)) # Output: 1
The time complexity of this approach is O(n) because we may need to iterate through the entire array in the worst case. The space complexity is O(1) as we are not using any additional space.
Potential edge cases include:
These cases are handled by the algorithm as it iterates through the array and returns the appropriate index.
To test the solution comprehensively, we can use a variety of test cases:
def test_find_insert_position():
assert find_insert_position([1, 2, 3, 5, 7], 4) == 3
assert find_insert_position([1, 2, 3], 2) == 1
assert find_insert_position([], 1) == 0
assert find_insert_position([1, 3, 5], 0) == 0
assert find_insert_position([1, 3, 5], 6) == 3
print("All tests passed.")
test_find_insert_position()
When approaching such problems, it's essential to understand the requirements and constraints. Start with a simple solution and then optimize it. Practice similar problems to improve problem-solving skills and become familiar with common patterns and algorithms.
Understanding and solving the lower bound problem is crucial for various applications in computer science. By practicing and exploring different approaches, you can improve your problem-solving skills and become more proficient in algorithm design.