Binary Search Iterative Approach in DSA Go - Time & Space Complexity

Time Complexity: Binary Search Iterative Approach

O(log n)

Understanding Time Complexity

We want to understand how the time taken by binary search changes as the list size grows.

Specifically, how many steps does it take to find a number in a sorted list?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func binarySearch(arr []int, target int) int {
    low, high := 0, len(arr)-1
    for low <= high {
        mid := low + (high-low)/2
        if arr[mid] == target {
            return mid
        } else if arr[mid] < target {
            low = mid + 1
        } else {
            high = mid - 1
        }
    }
    return -1
}
    

This code searches for a target number in a sorted list by repeatedly dividing the search range in half.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The loop that halves the search range each time.
• How many times: The loop runs until the search range is empty, roughly halving the size each time.

How Execution Grows With Input

Each step cuts the list size in half, so the number of steps grows slowly as the list grows.

Input Size (n)	Approx. Operations
10	About 4 steps
100	About 7 steps
1000	About 10 steps

Pattern observation: Doubling the input size adds only one extra step.

Final Time Complexity

Time Complexity: O(log n)

This means the steps needed grow slowly, increasing by one step each time the list size doubles.

Common Mistake

[X] Wrong: "Binary search checks every element one by one, so it is O(n)."

[OK] Correct: Binary search does not check every element; it cuts the search area in half each time, so it takes far fewer steps than checking all elements.

Interview Connect

Knowing how binary search scales helps you explain efficient searching in sorted data, a common skill interviewers look for.

Self-Check

"What if the list was not sorted? How would the time complexity of searching change?"

Practice

1. What is the main purpose of the mid variable in the iterative binary search algorithm?

easy

A. To keep track of the number of iterations

B. To store the target value to search

C. To find the middle index of the current search range

D. To hold the entire array being searched

Solution

1. Step 1: Understand the role and importance of mid in binary search

   The mid variable calculates the middle index between the current low and high indices to split the search range. By checking the element at mid, the algorithm decides whether to search the left or right half next, making the search efficient.

2. Final Answer:

   To find the middle index of the current search range -> Option C

3. Quick Check:

   mid = middle index [OK]

Hint: Mid splits search range in half each step [OK]

Common Mistakes:

• Confusing mid with target value
• Using mid as a counter instead of index
• Not updating mid inside the loop

2. Which of the following is the correct way to calculate the middle index mid in Go to avoid integer overflow?

easy

A. mid := (low + high) / 2

B. mid := (high - low) / 2

C. mid := (low - high) / 2

D. mid := low + (high - low) / 2

Solution

1. Step 1: Understand integer overflow risk and safe calculation

   Adding low + high directly can overflow if both are large integers. Calculating mid as low + (high - low) / 2 avoids overflow by subtracting first.

2. Final Answer:

   mid := low + (high - low) / 2 -> Option D

3. Quick Check:

   Safe mid calculation avoids overflow [OK]

Hint: Use low + (high - low)/2 to prevent overflow [OK]

Common Mistakes:

• Using (low + high)/2 directly causing overflow
• Subtracting incorrectly causing negative mid
• Confusing order of operations

3. What will be the output of the following Go code snippet?

package main
import "fmt"

func binarySearch(arr []int, target int) int {
    low, high := 0, len(arr)-1
    for low <= high {
        mid := low + (high - low) / 2
        if arr[mid] == target {
            return mid
        } else if arr[mid] < target {
            low = mid + 1
        } else {
            high = mid - 1
        }
    }
    return -1
}

func main() {
    arr := []int{2, 4, 6, 8, 10}
    result := binarySearch(arr, 6)
    fmt.Println(result)
}

medium

A. 3

B. 2

C. -1

D. 0

Solution

1. Step 1: Trace the binary search for target 6 and verify return

   Array is [2,4,6,8,10]. Initial low=0, high=4. mid=2. arr[2]=6 matches target. Function returns mid = 2 immediately.

2. Final Answer:

   2 -> Option B

3. Quick Check:

   Index of 6 in array = 2 [OK]

Hint: Check mid index value equals target first [OK]

Common Mistakes:

• Returning value instead of index
• Off-by-one errors in mid calculation
• Not updating low/high correctly

4. Identify the bug in this iterative binary search code snippet in Go:

func binarySearch(arr []int, target int) int {
    low, high := 0, len(arr)
    for low <= high {
        mid := low + (high - low) / 2
        if arr[mid] == target {
            return mid
        } else if arr[mid] < target {
            low = mid + 1
        } else {
            high = mid - 1
        }
    }
    return -1
}

medium

A. The initial value of high should be len(arr) - 1

B. The loop condition should be low < high

C. The mid calculation causes overflow

D. The function should return arr[mid] instead of mid

Solution

1. Step 1: Check high initialization and correct it

   High is set to len(arr), which is out of array index range (max index is len(arr)-1). Set high to len(arr) - 1 to avoid index out of range error when accessing arr[mid].

2. Final Answer:

   The initial value of high should be len(arr) - 1 -> Option A

3. Quick Check:

   High = len(arr)-1 fixes index error [OK]

Hint: High index must be last valid array index [OK]

Common Mistakes:

• Setting high to len(arr) causing runtime error
• Using wrong loop condition
• Returning value instead of index

5. Given a sorted array with possible duplicate elements, how can you modify the iterative binary search to find the first occurrence of a target value?

hard

A. When arr[mid] == target, update high = mid - 1 and continue searching

B. Return mid immediately when arr[mid] == target

C. When arr[mid] == target, update low = mid + 1 and continue searching

D. Use linear search after binary search to find first occurrence

Solution

1. Step 1: Understand duplicates issue and modify search logic

   Standard binary search returns any occurrence, not necessarily the first. When arr[mid] == target, move high to mid - 1 to continue searching left side for earlier occurrence, but store mid as potential answer.

2. Final Answer:

   When arr[mid] == target, update high = mid - 1 and continue searching -> Option A

3. Quick Check:

   Adjust high to find first occurrence [OK]

Hint: Move high left on match to find first occurrence [OK]

Common Mistakes:

• Returning immediately on first match
• Moving low right on match losing first occurrence
• Using linear search unnecessarily