Overlapping Subproblems and Optimal Substructure in DSA Typescript - Practice Problems & Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Fibonacci with Memoization

What is the output of the following TypeScript code that calculates Fibonacci numbers using memoization?

DSA Typescript

function fib(n: number, memo: {[key: number]: number} = {}): number {
  if (n <= 1) return n;
  if (memo[n] !== undefined) return memo[n];
  memo[n] = fib(n - 1, memo) + fib(n - 2, memo);
  return memo[n];
}
console.log(fib(5));

A 13

B 5

C 8

D 3

💡 Hint

Remember that Fibonacci sequence starts with 0, 1, 1, 2, 3, 5...

The function uses memoization to efficiently calculate Fibonacci numbers. fib(5) returns 5 because the sequence is 0,1,1,2,3,5...

🧠 Conceptual

intermediate

Identifying Overlapping Subproblems

Which of the following problems best demonstrates the concept of overlapping subproblems?

A Computing Fibonacci numbers using recursion without memoization

B Finding the shortest path in a graph using Dijkstra's algorithm

C Sorting an array using quicksort

D Calculating factorial of a number using recursion

💡 Hint

Overlapping subproblems means the same smaller problems are solved multiple times.

Computing Fibonacci numbers recursively without memoization recalculates the same values many times, showing overlapping subproblems.

🔧 Debug

advanced

Fix the Memoization Bug

The following TypeScript code tries to use memoization for Fibonacci but produces wrong output. What is the output when calling fib(4)?

DSA Typescript

function fib(n: number, memo: {[key: number]: number} = {}): number {
  if (n <= 1) return n;
  if (memo[n] !== undefined) return memo[n];
  memo[n] = fib(n - 1, memo) + fib(n - 2, memo);
  return memo[n];
}
console.log(fib(4));

A 4

B 5

C undefined

D 3

💡 Hint

Check how memoization cache is checked before recursive calls.

fib(4) = fib(3) + fib(2) = 2 + 1 = 3. The code works correctly and outputs 3.

🚀 Application

advanced

Optimal Substructure in Coin Change Problem

Given unlimited coins of denominations [1, 3, 4], what is the minimum number of coins needed to make amount 6? Use the optimal substructure property.

A 4

B 3

C 2

D 1

💡 Hint

Try to break amount 6 into smaller amounts and combine their solutions.

Minimum coins for 6 is 2: using coins 3 + 3 or 4 + 1 + 1 is more than 2 coins, so 3+3 is optimal.

🧠 Conceptual

expert

Why Dynamic Programming Requires Both Properties

Why does dynamic programming require both overlapping subproblems and optimal substructure properties?

A Because overlapping subproblems allow reuse of solutions and optimal substructure ensures global solution from local solutions

B Because overlapping subproblems guarantee no recursion and optimal substructure guarantees sorting

C Because overlapping subproblems mean problems are independent and optimal substructure means problems are unrelated

D Because overlapping subproblems allow parallel processing and optimal substructure allows random access

💡 Hint

Think about how dynamic programming builds solutions efficiently.

Dynamic programming works by solving smaller overlapping problems once and combining their optimal solutions to solve the bigger problem.

Practice

1. Which of the following best describes the overlapping subproblems property in dynamic programming?

easy

A. The problem requires sorting elements before solving.

B. The problem has a unique solution without repeating any subproblems.

C. The problem can be broken down into smaller subproblems that are solved multiple times.

D. The problem can only be solved using recursion without memoization.

Solution

1. Step 1: Understand overlapping subproblems

   Overlapping subproblems means the same smaller problems appear multiple times during solving.

2. Step 2: Match definition with options

   The problem can be broken down into smaller subproblems that are solved multiple times. correctly states that subproblems repeat and can be reused.

3. Final Answer:

   The problem can be broken down into smaller subproblems that are solved multiple times. -> Option C

4. Quick Check:

   Overlapping subproblems = repeated smaller problems [OK]

Hint: Look for repeated smaller problems solved multiple times [OK]

Common Mistakes:

• Confusing overlapping subproblems with unique subproblems
• Thinking sorting is required for overlapping subproblems
• Assuming recursion alone solves overlapping subproblems

2. Which TypeScript syntax correctly declares a memoization object for storing subproblem results?

easy

A. let memo = new Map();

B. const memo: number[] = [];

C. var memo = {};

D. const memo: string = '';

Solution

1. Step 1: Identify suitable data structure for memoization

   Memoization stores key-value pairs of subproblems and results, so Map is ideal.

2. Step 2: Check options for correct TypeScript syntax

   let memo = new Map(); uses Map which is correct for memoization.

3. Final Answer:

   let memo = new Map<number, number>(); -> Option A

4. Quick Check:

   Memoization uses Map for key-value storage [OK]

Hint: Use Map for flexible key-value memo storage in TypeScript [OK]

Common Mistakes:

• Using empty object without type safety
• Declaring memo as a string instead of a collection
• Using array when keys are not sequential indices

3. Consider this TypeScript function using memoization for Fibonacci numbers:

function fib(n: number, memo = new Map()): number {
  if (n <= 1) return n;
  if (memo.has(n)) return memo.get(n)!;
  const result = fib(n - 1, memo) + fib(n - 2, memo);
  memo.set(n, result);
  return result;
}
console.log(fib(5));

What is the output printed?

medium

A. 8

B. 5

C. 3

D. Error

Solution

1. Step 1: Understand Fibonacci calculation with memoization

   fib(5) = fib(4) + fib(3), recursively calculated and stored to avoid repeats.

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 B

4. Quick Check:

   fib(5) = 5 [OK]

Hint: Recall Fibonacci sequence: 0,1,1,2,3,5... [OK]

Common Mistakes:

• Confusing fib(5) with fib(6)
• Forgetting base cases n <= 1
• Assuming memo is reset each call

4. The following TypeScript code attempts to compute Fibonacci numbers with memoization but has a bug:

function fib(n: number, memo = new Map()): number {
  if (n <= 1) return n;
  if (memo.has(n)) return memo.get(n)!;
  const result = fib(n - 1) + fib(n - 2);
  memo.set(n, result);
  return result;
}
console.log(fib(5));

What is the main issue causing incorrect behavior?

medium

A. Memo object is not passed in recursive calls, so memoization fails.

B. Base case condition is incorrect for n <= 1.

C. Map is not initialized properly.

D. Return statement is missing.

Solution

1. Step 1: Analyze recursive calls and memo usage

   Recursive calls fib(n - 1) and fib(n - 2) do not pass the memo Map, so each call creates a new Map.

2. Step 2: Understand impact on memoization

   Without passing memo, results are not stored or reused, causing repeated calculations and no optimization.

3. Final Answer:

   Memo object is not passed in recursive calls, so memoization fails. -> Option A

4. Quick Check:

   Pass memo in recursive calls to reuse stored results [OK]

Hint: Always pass memo object in recursive calls for memoization [OK]

Common Mistakes:

• Not passing memo in recursive calls
• Changing base case unnecessarily
• Assuming Map auto-shares across calls

5. You want to find the minimum cost path in a grid from top-left to bottom-right, moving only right or down. The cost grid is:

const cost = [
  [1, 3, 5],
  [2, 1, 2],
  [4, 3, 1]
];

Which approach best uses overlapping subproblems and optimal substructure to solve this efficiently?

hard

A. Use a greedy approach picking the smallest adjacent cost at each step.

B. Use recursion without memoization to explore all paths.

C. Sort the grid and pick the smallest values.

D. Use dynamic programming to store minimum cost for each cell based on previous cells.

Solution

1. Step 1: Identify problem properties

   Minimum cost path has optimal substructure: cost to reach a cell depends on minimum cost to reach adjacent cells.

2. Step 2: Choose approach using overlapping subproblems

   Dynamic programming stores results for each cell to avoid recomputing paths (overlapping subproblems).

3. Final Answer:

   Use dynamic programming to store minimum cost for each cell based on previous cells. -> Option D

4. Quick Check:

   DP uses overlapping subproblems + optimal substructure [OK]

Hint: Use DP to store and reuse subproblem results for grid paths [OK]

Common Mistakes:

• Using recursion without memoization causing slow performance
• Assuming greedy always finds minimum cost
• Sorting grid values unrelated to path costs