Algorithm Interview Questions

Preparing for a job interview in Algorithm. I am sure you want to know the most common 2026 Algorithm Interview Questions and Answers to help you quickly crack the Algorithm Interview. Below is the list of top Algorithm Interview Questions and answers at your rescue.

The following questions and answers help freshers and experienced professionals prepare for technical interviews effectively.

Part 1 – Basic Algorithm Interview Questions and Answers

This first part covers basic Interview Questions and Answers.

Q1. Write an algorithm to reverse a string. For example, if my string is “vahbunA,” my result will be “Anubhav.”

Answer:

• Step 1: Start
• Step 2: Take two-variable i and j.
• Step 3: j is positioned on the last character (Technically, we can do this by length(string)-1)
• Step 4: I is positioned on the first character (we can do this by the string[0])
• Step 5: String[i] is interchanged String[j]
• Step 6: Increment i by 1
• Step 7: Increment j by 1
• Step 8: If ‘I’> ‘j,’ then go to Step 3
• Step 9: Stop

Example

Input: “vahbunA”
Output: “Anubhav”

Q2. Write an algorithm to insert a node in the linked list, assuming the linked list is sorted already?

Answer:

Case 1: If the linked list is empty, return the node as head.

Code:

New_node-> Next = head;
head = New_node

Case 2: Insert node in the middle

Code:

While (P!= insert_position)
{
P= p->Next;
}
Store_next= P->Next;
P->Next = New_Node;
New_Node->Next = Store_next;

Case 3: Insert a node at the end

Code:

While (P->next!= null)
{
P= P->Next;
}
P->Next = New_Node;
New_Node->Next = null;

Q3. Write an algorithm for Bubble Sort?

Answer:

We will implement the bubble sort algorithm through C language.

Step 1: Repeat Step 2 and Step 3

for I = 1 to 10

Step 2:

Set j = 1

Step 3: Repeat

while j <= n (Where n is number of elements in the array)
{ If a[i] < a[j] Then interchange a[i] and a[j] [End of if] }
Set j = j + 1
[End of inner loop] [End of step 1 outer loop]

Step 4:

Exit

Q4. Write an algorithm for Heap Sort?

Answer:

Heap Sort uses a binary heap data structure to sort elements efficiently.

• Step 1: Since the tree satisfies the max-Heap property, the essential item is stored at the root node.
• Step 2: Remove the root element and put at the end of the array (nth position) put the last item of the tree (heap) at the vacant place.
• Step 3: Reduce the heap’s size by 1 and heap the root element again to have the highest element at the root.
• Step 4: The process is repeated until all the items on the list are sorted.

Part 2 – Advanced Algorithm Interview Questions and Answers

Let us now have a look at the advanced Interview Questions.

Q5. Write an algorithm for the Fibonacci search?

Answer:

Fibonacci Search is an efficient searching algorithm that works on sorted arrays.

Step 1: A is

sorted_int_array;

Step 2: take one variable c

Step 3:

Fib2 = 1, Fib1 = 1 and fib = 2

Step 4:

While fib < n do (where n is number of element in list)

Step 5: Assign the variable.

Fib2 = Fib1
Fib1 = Fib
Fib = Fib1 + Fib2
End while

Step 6: Assign the value to a temporary variable

I = 0, offset = 0;

Step 7:

While Fib > 1 do
I = min (offset + Fib2, n)
If c < A[i] then
Fib = Fib2
Fib1 = Fib1 – Fib2
Fib2 = Fib – Fib1
Else if c > A[i] then
Fib = Fib1;
Fib1 = Fib2;
Fib2 = Fib – Fib1;
Offset = I;
Else
Return true
End if
End while
Return false

Q6. Write an algorithm of push and pop operation in the stack?

Answer:

For Push Operation
Procedure Add( Item, Stack, N, Top)
(Insert ‘Item’ into the ‘stack’ of maximum size’ n,’ top is the number of elements currently in ‘Stack’)

Step 1: Check Stack is overflowing.

If (Top >= N)
Stack is overflow
Exit

Step 2: If the stack does not overflow, increment the loop

Top = Top + 1

Step 3: Insert the element.

Stack [Top] = Item

Step 4:

Exit

For POP Operation:

Step 1: Check Stack is underflow means empty.

If (Top <= 0)
Stack is empty
Exit

Step 2: If the stack is not underflow, then delete the element

Item = stack[top]

Step 3: decrementing the top value

Top = Top – 1

Step 4:

Exit

Q7. Write an algorithm for inserting and deleting operations in the queue?

Answer:

Insertion Operation (Enqueue)

Procedure add(queue, F, R, N, item)
(This will insert ‘item’ in the ‘queue’ after ‘R’ (rare) where ‘n’ is the array size.)

Step 1: Check Queue is overflow means the queue is full.

If (R >= N)
Queue is full
Exit

Step 2: If the Queue is not overflowed, then increment the loop

R = R + 1

Step 3: Insert an element into the queue

Queue[R] = item

Step 4: Setting the ‘F’ (Front) Pointer

If ( F = 0)
F = 1
Exit

For Deletion operation in the queue
Procedure delete(queue, F, R, item)
(Delete ‘item’ from the ‘stack,’ ‘F’ is the Front-end pointer, and ‘R’ is the rare end pointer.

Step 1: Check Queue is underflow means empty.

If ( R < = 0)
Queue is empty
Exit

Step 2: Deleting an element from the queue

Item = queue [F]

Step 3: Incrementing the value of F

F = F+1

Step 4: Checking the empty queue

If (F > R)
Then F = R = 0
Exit

Q8. Write an algorithm to find the minimum depth of a binary tree?

Answer:

Let “node” be the pointer to the root node of a subtree.

• Step 1: If the node is equal to Null, return 0
• Step 2: If the node is a leaf node, return 1.
• Step 3: Recursively, find the minimum depth of the left and right sub-tree; let it be left Min Depth and right min depth, respectively.
• Step 4: To get the tree’s minimum height rooted at the node, we will take a minimum of left min depth and right min depth and 1 for the root node.

Program:

Procedure minDepth ( Node )

Step 1:

if ( root = null)
Return 0

Step 2:

if (root -> Left = Null and root -> right = Null )
Return 1

Step 3:

if (root -> left is not null)
Return minDepth (root -> right ) + 1;

Step 4:

If (root -> Right is not null )
Return minDepth ( root -> left) + 1;

Step 5:

return min(minDepth (root -> left), minDepth (root -> right)) + 1

Q9. What is the difference between Time Complexity and Space Complexity?

Answer:

Time Complexity measures the amount of time an algorithm takes to execute as the input size grows, while Space Complexity measures the amount of memory used by the algorithm.

Example

• Binary Search Time Complexity: O(log n)
• Merge Sort Space Complexity: O(n)

Common Time Complexities:

Complexity	Description
O(1)	Constant Time
O(log n)	Logarithmic Time
O(n)	Linear Time
O(n log n)	Linearithmic Time
O(n²)	Quadratic Time

Q10. What is Recursion? Explain with an example.

Answer:

Recursion is a programming technique in which a function calls itself repeatedly to solve smaller instances of the same problem. Recursive solutions are widely used in tree traversal, sorting, searching, and mathematical computations.

Example: Factorial Using Recursion

n!=n×(n−1)!n! = n \times (n-1)!n!=n×(n−1)!

Algorithm

1. If n = 0 or n = 1, return 1
2. Else return n * factorial(n-1)

Code

Function Factorial(n)

If (n == 0 OR n == 1)
Return 1

Return n * Factorial(n – 1)

Q11. Explain the Divide and Conquer technique.

Answer:

Divide and Conquer is an algorithm design technique that divides a problem into smaller subproblems, solves them recursively, and combines their results.

Steps

1. Divide the problem into smaller parts
2. Solve each subproblem recursively
3. Combine the solutions

Examples

• Merge Sort
• Quick Sort
• Binary Search

Q12. Write an algorithm for Binary Search.

Answer:

Binary Search is an efficient searching algorithm used to search an element in a sorted array. Instead of checking every element one by one, Binary Search repeatedly divides the array into two halves.

This significantly improves performance compared to Linear Search.

Working Principle

1. Find the middle element of the array.
2. Compare the target value with the middle element.
3. If both are equal, the element is found.
4. If the target is smaller, search the left half.
5. If the target is greater, search the right half.
6. Repeat the process until the element is found or the search space becomes empty.

Algorithm

Procedure BinarySearch(Array, n, key)

low = 0
high = n – 1

While (low <= high)
{
mid = (low + high) / 2

If (Array[mid] == key)
Return mid

Else If (Array[mid] < key)
low = mid + 1

Else
high = mid – 1
}

Return -1

Advantages

Faster than Linear Search

Efficient for large datasets

Reduces search operations significantly

Time Complexity

O(log⁡n)O(\log n)O(logn)

Conclusion

Algorithm interview questions test logical thinking, optimization techniques, and understanding of data structures. Practicing sorting, searching, recursion, trees, stacks, queues, and linked list algorithms regularly will help improve coding efficiency and interview performance.