Memoization to Optimize Recursion in DSA Java - Time & Space Complexity

Time Complexity: Memoization to Optimize Recursion

O(n)

Understanding Time Complexity

We want to see how adding memoization changes the speed of recursive functions.

How does remembering past answers help us do less work?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

public class Fibonacci {
    private int[] memo;

    public Fibonacci(int n) {
        memo = new int[n + 1];
    }

    public int fib(int n) {
        if (n <= 1) return n;
        if (memo[n] != 0) return memo[n];
        memo[n] = fib(n - 1) + fib(n - 2);
        return memo[n];
    }
}

This code calculates Fibonacci numbers using recursion and stores results to avoid repeated work.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls to fib for smaller numbers.
• How many times: Without memo, calls grow exponentially; with memo, each number is computed once.

How Execution Grows With Input

With memoization, each Fibonacci number from 0 to n is calculated once and stored.

Input Size (n)	Approx. Operations
10	About 10 calculations
100	About 100 calculations
1000	About 1000 calculations

Pattern observation: The work grows roughly in a straight line with input size.

Final Time Complexity

Time Complexity: O(n)

This means the time to compute grows directly with the input size, much faster than without memoization.

Common Mistake

[X] Wrong: "Memoization makes recursion instantly fast for all problems."

[OK] Correct: Memoization only helps if the problem has overlapping subproblems; otherwise, it won't reduce calls.

Interview Connect

Understanding memoization shows you can improve slow recursive solutions by saving work, a key skill in many coding challenges.

Self-Check

"What if we removed the memo array and just used plain recursion? How would the time complexity change?"

Practice

1. What is the main purpose of using memoization in recursive functions?

easy

A. To store results of expensive function calls and reuse them

B. To make the code shorter and simpler

C. To avoid using loops in recursion

D. To increase the number of recursive calls

Solution

1. Step 1: Understand recursion inefficiency

   Recursive functions often repeat the same calculations multiple times, which wastes time.

2. Step 2: Role of memoization

   Memoization saves the results of these repeated calculations so they can be reused instead of recalculated.

3. Final Answer:

   To store results of expensive function calls and reuse them -> Option A

4. Quick Check:

   Memoization = Store and reuse results [OK]

Hint: Memoization saves repeated results to speed recursion [OK]

Common Mistakes:

• Thinking memoization makes code shorter
• Confusing memoization with iteration
• Believing memoization increases recursion calls

2. Which of the following Java code snippets correctly declares a memoization cache for storing integer results by input integer keys?

easy

A. Map memo = new HashMap<>();

B. List<Integer> memo = new ArrayList<>();

C. int[] memo = new int();

D. HashSet<Integer> memo = new HashSet<>();

Solution

1. Step 1: Identify suitable data structure for key-value pairs

   Memoization requires storing results by input keys, so a Map is ideal.

2. Step 2: Check Java syntax correctness

   Map memo = new HashMap<>(); uses Map with HashMap, which is correct syntax for memoization cache.

3. Final Answer:

   Map memo = new HashMap<>(); -> Option A

4. Quick Check:

   Memo cache = Map for key-value pairs [OK]

Hint: Use Map for key-value memo cache in Java [OK]

Common Mistakes:

• Using List instead of Map for memoization
• Incorrect array declaration syntax
• Using Set which stores only keys, no values

3. What will be the output of the following Java code using memoization for Fibonacci numbers?

import java.util.*;
public class FibMemo {
  static Map memo = new HashMap<>();
  public static int fib(int n) {
    if (n <= 1) return n;
    if (memo.containsKey(n)) return memo.get(n);
    int result = fib(n-1) + fib(n-2);
    memo.put(n, result);
    return result;
  }
  public static void main(String[] args) {
    System.out.println(fib(5));
  }
}

medium

A. Error at runtime

B. 8

C. 5

D. 13

Solution

1. Step 1: Understand Fibonacci sequence calculation

   fib(5) = fib(4) + fib(3), and so on, with base cases fib(0)=0, fib(1)=1.

2. Step 2: Calculate fib(5) with memoization

   fib(2)=1, fib(3)=2, fib(4)=3, fib(5)=5. Memoization avoids repeated calls but result is same.

3. Final Answer:

   5 -> Option C

4. Quick Check:

   fib(5) = 5 [OK]

Hint: Recall Fibonacci(5) = 5, memoization doesn't change result [OK]

Common Mistakes:

• Confusing Fibonacci values for wrong n
• Assuming memoization changes output
• Thinking code causes runtime error

4. Identify the error in this Java memoized factorial function:

import java.util.*;
public class FactorialMemo {
  static Map memo = new HashMap<>();
  public static int fact(int n) {
    if (n == 0) return 1;
    if (memo.containsKey(n)) return memo.get(n);
    int result = n * fact(n - 1);
    memo.put(n, result);
    return result;
  }
  public static void main(String[] args) {
    System.out.println(fact(-1));
  }
}

medium

A. Memo map is not initialized properly

B. fact method should return long instead of int

C. Incorrect multiplication order in recursion

D. No base case for negative input causes infinite recursion

Solution

1. Step 1: Analyze base cases and input handling

   Base case handles n == 0 only, but negative n causes no stop condition.

2. Step 2: Effect of negative input

   Calling fact(-1) leads to infinite recursion because n never reaches 0 or memoized value.

3. Final Answer:

   No base case for negative input causes infinite recursion -> Option D

4. Quick Check:

   Missing negative base case = infinite recursion [OK]

Hint: Check base cases cover all input ranges [OK]

Common Mistakes:

• Ignoring negative input handling
• Assuming memo initialization error
• Thinking multiplication order causes error

5. You want to optimize a recursive function that calculates the number of ways to climb n stairs taking 1 or 2 steps at a time. Which memoization approach below correctly implements this in Java?

hard

A. Use a List<Integer> to store results; always calculate recursively without checking cache.

B. Use a Map to store results; check cache before recursive calls; store results after calculation.

C. Use an int array without initializing values; store results only after all recursion ends.

D. Use a Set<Integer> to store visited stairs; return size of set as result.

Solution

1. Step 1: Understand problem and memoization needs

   We must store number of ways for each stair number to avoid repeated calculations.

2. Step 2: Evaluate memoization approaches

   Use a Map to store results; check cache before recursive calls; store results after calculation. uses Map to store and check results before recursion, which is correct memoization practice.

3. Final Answer:

   Use a Map to store results; check cache before recursive calls; store results after calculation. -> Option B

4. Quick Check:

   Memo = Map with cache check and store [OK]

Hint: Check cache before recursion, store after calculation [OK]

Common Mistakes:

• Not checking cache before recursive calls
• Using List or Set incorrectly for memoization
• Storing results too late or not at all