Fast Exponentiation Power in Log N in DSA Typescript - Time & Space Complexity

Time Complexity: Fast Exponentiation Power in Log N

O(log n)

Understanding Time Complexity

We want to understand how fast the power calculation grows as the exponent gets bigger.

How does the number of steps change when we calculate x to the power n quickly?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

function fastPower(x: number, n: number): number {
  if (n === 0) return 1;
  const half = fastPower(x, Math.floor(n / 2));
  if (n % 2 === 0) return half * half;
  else return half * half * x;
}
    

This code calculates x raised to the power n using a fast method by splitting the problem in half each time.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls that split the exponent n by half each time.
• How many times: About log base 2 of n times, because n is divided by 2 each call.

How Execution Grows With Input

Each time we call the function, the exponent gets cut in half, so the number of steps grows slowly.

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

Pattern observation: Doubling n adds only one more step, so growth is very slow compared to n itself.

Final Time Complexity

Time Complexity: O(log n)

This means the number of steps grows slowly as the exponent gets bigger, making it very efficient.

Common Mistake

[X] Wrong: "Calculating power always takes n steps because we multiply x n times."

[OK] Correct: This method uses splitting and recursion to reduce steps to about log n, not n, so it is much faster.

Interview Connect

Understanding this fast power method shows you can improve simple tasks by clever thinking, a skill valued in interviews and real projects.

Self-Check

"What if we changed the method to use a loop instead of recursion? How would the time complexity change?"

Practice

1. What is the main advantage of using Fast Exponentiation to calculate a^b compared to the simple repeated multiplication?

easy

A. It reduces the time complexity from linear to logarithmic in terms of exponent.

B. It uses less memory by storing all intermediate powers.

C. It avoids using multiplication altogether.

D. It only works for small exponents.

Solution

1. Step 1: Understand simple exponentiation complexity

   Simple repeated multiplication calculates a^b by multiplying a by itself b times, which takes O(b) time.

2. Step 2: Analyze fast exponentiation complexity

   Fast exponentiation uses the idea of squaring and reduces the number of multiplications to O(log b), making it much faster for large exponents.

3. Final Answer:

   It reduces the time complexity from linear to logarithmic in terms of exponent. -> Option A

4. Quick Check:

   Fast exponentiation = O(log n) time [OK]

Hint: Fast exponentiation halves exponent each step [OK]

Common Mistakes:

• Thinking it avoids multiplication
• Assuming it uses more memory
• Believing it only works for small exponents

2. Which of the following TypeScript function signatures correctly represents a fast exponentiation function for integers a and b?

easy

A. function fastPower(a: number): number

B. function fastPower(a: string, b: number): string

C. function fastPower(a: number, b: number): void

D. function fastPower(a: number, b: number): number

Solution

1. Step 1: Check parameter types

   The function needs two inputs: base a and exponent b, both numbers.

2. Step 2: Check return type

   The function returns the result of a^b, which is a number.

3. Final Answer:

   function fastPower(a: number, b: number): number -> Option D

4. Quick Check:

   Correct signature = function fastPower(a: number, b: number): number [OK]

Hint: Function needs two number inputs and returns number [OK]

Common Mistakes:

• Using string types for numbers
• Missing one parameter
• Returning void instead of number

3. What is the output of the following TypeScript code?

function fastPower(a: number, b: number): number {
  if (b === 0) return 1;
  let half = fastPower(a, Math.floor(b / 2));
  if (b % 2 === 0) return half * half;
  else return half * half * a;
}
console.log(fastPower(2, 5));

medium

A. 25

B. 10

C. 32

D. 16

Solution

1. Step 1: Trace recursive calls for b=5

   fastPower(2,5) calls fastPower(2,2), which calls fastPower(2,1), which calls fastPower(2,0)=1.

2. Step 2: Calculate results bottom-up

   fastPower(2,1) = 1*1*2 = 2
   fastPower(2,2) = 2*2 = 4
   fastPower(2,5) = 4*4*2 = 32

3. Final Answer:

   32 -> Option C

4. Quick Check:

   2^5 = 32 [OK]

Hint: Remember 2^5 = 32, check recursive halving [OK]

Common Mistakes:

• Confusing multiplication order
• Forgetting to multiply by base when exponent is odd
• Incorrect base case handling

4. Identify the bug in this fast exponentiation function and select the correct fix:

function fastPower(a: number, b: number): number {
  if (b === 0) return 0;
  let half = fastPower(a, Math.floor(b / 2));
  if (b % 2 === 0) return half * half;
  else return half * half * a;
}

medium

A. Change recursive call to fastPower(a, b - 1)

B. Change base case return from 0 to 1

C. Remove the multiplication by 'a' when exponent is odd

D. Change Math.floor to Math.ceil in recursive call

Solution

1. Step 1: Check base case correctness

   When exponent is 0, a^0 should return 1, but code returns 0, which is incorrect.

2. Step 2: Identify fix for base case

   Changing return value in base case from 0 to 1 fixes the function to correctly compute powers.

3. Final Answer:

   Change base case return from 0 to 1 -> Option B

4. Quick Check:

   Base case must return 1 [OK]

Hint: Base case for exponent 0 must return 1 [OK]

Common Mistakes:

• Returning 0 for exponent 0
• Changing recursive call incorrectly
• Removing multiplication for odd exponent

5. You want to compute (3^13) % 7 efficiently using fast exponentiation with modulo to avoid large numbers. Which modification to the fastPower function is correct?

hard

A. Apply modulo after each multiplication: (half * half) % mod and (half * half * a) % mod

B. Apply modulo only once at the end of recursion

C. Apply modulo only to the base a before recursion

D. No modulo needed if exponent is small

Solution

1. Step 1: Understand modulo property in exponentiation

   To avoid large intermediate values, modulo must be applied after every multiplication step.

2. Step 2: Modify function to apply modulo after multiplications

   Applying modulo after half * half and half * half * a keeps numbers small and correct.

3. Final Answer:

   Apply modulo after each multiplication: (half * half) % mod and (half * half * a) % mod -> Option A

4. Quick Check:

   Modulo after each multiply keeps numbers small [OK]

Hint: Modulo after every multiply prevents overflow [OK]

Common Mistakes:

• Applying modulo only once at end
• Applying modulo only to base
• Ignoring modulo for small exponents