Memoization Top Down DP in DSA Java - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is memoization in the context of dynamic programming?

Memoization is a technique where we store the results of expensive function calls and reuse them when the same inputs occur again, avoiding repeated calculations.

beginner

How does top-down dynamic programming differ from bottom-up dynamic programming?

Top-down DP solves the problem by breaking it down recursively and storing results (memoization), while bottom-up DP solves smaller subproblems first and builds up to the solution iteratively.

beginner

In Java, what data structure is commonly used to store memoized results in top-down DP?

A common data structure is an array or a HashMap, depending on the problem's input size and type, to store computed results for subproblems.

intermediate

Why is memoization important in recursive solutions for problems like Fibonacci numbers?

Without memoization, recursive calls repeat the same calculations many times, causing exponential time complexity. Memoization reduces this to linear time by saving and reusing results.

beginner

What is a key step to implement memoization in a top-down DP approach?

Before computing a subproblem, check if its result is already stored. If yes, return it immediately; if no, compute, store, and then return the result.

What does memoization primarily help to avoid in recursive algorithms?

A Using extra memory

B Repeated calculations of the same subproblems

C Writing iterative code

D Sorting input data

Memoization stores results of subproblems to avoid recalculating them, improving efficiency.

In top-down DP, when do you store the result of a subproblem?

A After computing it for the first time

B Before computing it

C Only if the result is zero

D Never, results are recalculated

You store the result after computing it to reuse it later and avoid repeated work.

Which of these is a typical sign that memoization can improve a recursive solution?

A The function has no base case

B The function uses loops

C The function returns a constant value

D The same function calls repeat with the same inputs

Repeated calls with the same inputs indicate overlapping subproblems, ideal for memoization.

What is the time complexity improvement when using memoization on Fibonacci calculation?

A No improvement

B From linear to exponential

C From exponential to linear

D From quadratic to cubic

Memoization reduces Fibonacci calculation from exponential time to linear time.

Which data structure is NOT commonly used for memoization in Java?

A HashSet

B HashMap

C LinkedList

D Array

HashSet stores unique values but does not map inputs to results, so it is not used for memoization.

Explain how memoization works in a top-down dynamic programming approach with an example.

Think about how you save answers to smaller problems to use later.

Describe the difference between top-down memoization and bottom-up dynamic programming.

Consider how the problem is solved step-by-step in each approach.

Practice

1. What is the main purpose of memoization in top-down dynamic programming?

easy

A. To increase the size of the input for better accuracy

B. To sort the input data before processing

C. To store results of subproblems to avoid repeated calculations

D. To convert recursive code into iterative code

Solution

1. Step 1: Understand memoization role

   Memoization saves answers of smaller problems so they are not recalculated.

2. Step 2: Compare options

   Only To store results of subproblems to avoid repeated calculations correctly describes storing subproblem results to avoid repeats.

3. Final Answer:

   To store results of subproblems to avoid repeated calculations -> Option C

4. Quick Check:

   Memoization = store subproblem results [OK]

Hint: Memoization means saving answers to reuse later [OK]

Common Mistakes:

• Confusing memoization with sorting
• Thinking memoization changes recursion to iteration
• Believing memoization increases input size

2. Which of the following is the correct way to declare a memo array for Fibonacci numbers in Java?

easy

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

B. int memo = new int[n];

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

D. int[] memo = new int[n+1];

Solution

1. Step 1: Check Java array declaration syntax

   Correct syntax for array with size n+1 is: int[] memo = new int[n+1];

2. Step 2: Identify errors in other options

   int memo = new int[n]; declares int, not array; C misses size; D uses wrong constructor syntax.

3. Final Answer:

   int[] memo = new int[n+1]; -> Option D

4. Quick Check:

   Array declaration = int[] memo = new int[size] [OK]

Hint: Use int[] memo = new int[n+1] for memo arrays [OK]

Common Mistakes:

• Declaring memo as int instead of int[]
• Not specifying array size when creating
• Using parentheses instead of brackets for arrays

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

public class FibMemo {
  static int[] memo;
  public static int fib(int n) {
    if (n <= 1) return n;
    if (memo[n] != -1) return memo[n];
    memo[n] = fib(n-1) + fib(n-2);
    return memo[n];
  }
  public static void main(String[] args) {
    int n = 5;
    memo = new int[n+1];
    for (int i = 0; i <= n; i++) memo[i] = -1;
    System.out.println(fib(n));
  }
}

medium

A. 5

B. 8

C. 3

D. Error due to uninitialized memo

Solution

1. Step 1: Understand Fibonacci calculation with memo

   fib(5) = fib(4) + fib(3), recursively calculated and stored in memo.

2. Step 2: Calculate fib(5) step-by-step

   fib(0)=0, fib(1)=1, fib(2)=1, fib(3)=2, fib(4)=3, fib(5)=5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   fib(5) = 5 [OK]

Hint: Remember fib(5) = 5 in Fibonacci sequence [OK]

Common Mistakes:

• Confusing fib(5) with fib(6)
• Forgetting to initialize memo with -1
• Assuming memo causes errors if not initialized

4. Identify the bug in this memoized Fibonacci code snippet:

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

public static void main(String[] args) {
  int n = 4;
  int[] memo = new int[n+1];
  System.out.println(fib(n, memo));
}

medium

A. Memo array not initialized with -1, causing wrong cache checks

B. Base case should return 0 for n <= 1

C. Memo array size is too small

D. fib function missing return statement

Solution

1. Step 1: Check memo initialization and cache check

   Memo array defaults to 0; fib(0)=0, so memo[n]!=0 check fails for fib(0).

2. Step 2: Explain why -1 initialization is needed

   Initialize memo with -1 to distinguish uncalculated from zero results.

3. Final Answer:

   Memo array not initialized with -1, causing wrong cache checks -> Option A

4. Quick Check:

   Initialize memo with -1 to avoid false cache hits [OK]

Hint: Initialize memo with -1 to mark unvisited states [OK]

Common Mistakes:

• Assuming 0 means unvisited in memo
• Changing base case incorrectly
• Using wrong memo array size

5. Given the top-down memoized code for the coin change problem below, what is the output for coins = {1, 2} and amount = 4?

public class CoinChange {
  static int[] memo;
  public static int countWays(int amount, int[] coins) {
    if (amount == 0) return 1;
    if (amount < 0) return 0;
    if (memo[amount] != -1) return memo[amount];
    int ways = 0;
    for (int coin : coins) {
      ways += countWays(amount - coin, coins);
    }
    memo[amount] = ways;
    return ways;
  }
  public static void main(String[] args) {
    int[] coins = {1, 2};
    int amount = 4;
    memo = new int[amount + 1];
    for (int i = 0; i <= amount; i++) memo[i] = -1;
    System.out.println(countWays(amount, coins));
  }
}

hard

A. 4

B. 5

C. 3

D. 6

Solution

1. Step 1: Understand problem and recursion

   Count ways to make 4 using coins 1 and 2, summing ways for amount-coin recursively.

2. Step 2: Calculate ways for amount=4

   Ways(4) = Ways(3)+Ways(2); Ways(3)=Ways(2)+Ways(1); Ways(2)=Ways(1)+Ways(0); Ways(1)=Ways(0)+Ways(-1); base cases: Ways(0)=1, Ways(-1)=0.

3. Step 3: Calculate values bottom-up

   Ways(0)=1; Ways(1)=1+0=1; Ways(2)=1+1=2; Ways(3)=2+1=3; Ways(4)=3+2=5.

4. Step 4: Re-check sum

   Actually, the code counts permutations, so for amount=4 with coins {1,2}, total ways = 5.

5. Final Answer:

   5 -> Option B

6. Quick Check:

   Number of ways to make 4 with {1,2} = 5 [OK]

Hint: Count ways recursively, memoize to avoid repeats [OK]

Common Mistakes:

• Confusing combinations with permutations
• Not initializing memo with -1
• Miscounting base cases