Tabulation Bottom Up DP in DSA C++ - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Tabulation in Dynamic Programming?

Tabulation is a bottom-up approach in dynamic programming where we solve smaller subproblems first and store their results in a table (usually an array) to build up solutions to bigger problems.

beginner

How does Tabulation differ from Memoization?

Tabulation solves problems iteratively from the smallest subproblem up, filling a table. Memoization solves problems top-down using recursion and stores results to avoid repeated work.

intermediate

Why is Tabulation often preferred over Memoization?

Tabulation avoids recursion overhead, uses iteration, and often has better space and time efficiency because it fills the table in a controlled order without stack calls.

beginner

In Tabulation, what does the table usually represent?

The table stores the results of subproblems, where each entry corresponds to a solution for a smaller input or state, allowing reuse to build the final answer.

beginner

Give a simple example problem solved by Tabulation.

The Fibonacci sequence can be solved by Tabulation by filling an array from the base cases (fib(0)=0, fib(1)=1) up to fib(n), each entry as sum of two previous entries.

What is the main idea behind Tabulation in DP?

A Solve smaller problems first and build up

B Use recursion and store results

C Randomly guess answers

D Divide problem into unrelated parts

Tabulation solves smaller subproblems first and uses their answers to solve bigger problems.

Which approach uses iteration instead of recursion?

A Brute force

B Memoization

C Backtracking

D Tabulation

Tabulation uses iteration to fill the table bottom-up.

In Tabulation, what do we store in the table?

A Random values

B Function pointers

C Results of subproblems

D Input values

The table stores results of solved subproblems to avoid recomputation.

Which is a benefit of Tabulation over Memoization?

A Avoids recursion overhead

B Uses recursion stack

C Slower execution

D More memory usage

Tabulation avoids recursion overhead by using iteration.

What is the base case in Tabulation for Fibonacci numbers?

A fib(0)=1, fib(1)=1

B fib(0)=0, fib(1)=1

C fib(0)=1, fib(1)=0

D fib(0)=0, fib(1)=0

The base cases for Fibonacci are fib(0)=0 and fib(1)=1.

Explain how Tabulation solves a problem using bottom-up dynamic programming.

Think about filling a table from the base cases upwards.

Describe the difference between Memoization and Tabulation in dynamic programming.

Focus on the direction and method of solving subproblems.

Practice

1. What is the main idea behind Tabulation Bottom Up DP in dynamic programming?

easy

A. Build a table from the smallest subproblems up to the main problem

B. Solve the problem by recursion without storing results

C. Use random guesses to find the solution

D. Start solving from the largest problem and break it down

Solution

1. Step 1: Understand bottom-up approach

   Tabulation means filling a table starting from the smallest subproblems, so we solve all smaller parts first.

2. Step 2: Compare with other methods

   Unlike recursion without memory, bottom-up avoids repeated work by storing results in a table.

3. Final Answer:

   Build a table from the smallest subproblems up to the main problem -> Option A

4. Quick Check:

   Bottom-up DP = Build table from small to big [OK]

Hint: Think: solve small parts first, then bigger ones [OK]

Common Mistakes:

• Confusing bottom-up with recursion without memoization
• Thinking it starts from the largest problem
• Ignoring the use of a table to store results

2. Which of the following is the correct way to declare a DP table for Fibonacci numbers using tabulation in C++?

easy

A. int dp[]; dp[1] = 1;

B. int dp[100] = {0}; dp[1] = 1;

C. int dp[100]; dp[0] = 1;

D. int dp[100] = {1}; dp[2] = 1;

Solution

1. Step 1: Check array initialization

   int dp[100] = {0}; dp[1] = 1; initializes dp with zeros and sets dp[1] = 1, which is correct for Fibonacci base cases.

2. Step 2: Identify errors in other options

   int dp[]; dp[1] = 1; has no size for dp array, invalid in C++. int dp[100]; dp[0] = 1; sets dp[0] = 1 which is incorrect base case. int dp[100] = {1}; dp[2] = 1; initializes dp with 1 at dp[0], which is wrong.

3. Final Answer:

   int dp[100] = {0}; dp[1] = 1; -> Option B

4. Quick Check:

   Correct base initialization = int dp[100] = {0}; dp[1] = 1; [OK]

Hint: Initialize dp array with zeros and set base cases explicitly [OK]

Common Mistakes:

• Not specifying array size
• Wrong base case initialization
• Assuming default values without initialization

3. What will be the output of the following C++ code using bottom-up DP to find the 5th Fibonacci number?

int fib(int n) {
  int dp[6] = {0};
  dp[1] = 1;
  for (int i = 2; i <= n; i++) {
    dp[i] = dp[i-1] + dp[i-2];
  }
  return dp[n];
}

int main() {
  std::cout << fib(5);
  return 0;
}

medium

A. 5

B. 8

C. 3

D. 13

Solution

1. Step 1: Trace dp array filling

   dp[0]=0, dp[1]=1; then dp[2]=1, dp[3]=2, dp[4]=3, dp[5]=5 by adding previous two.

2. Step 2: Return dp[5]

   dp[5] is 5, so output is 5.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   Fibonacci(5) = 5 [OK]

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

Common Mistakes:

• Off-by-one errors in indexing
• Confusing dp[0] and dp[1] values
• Returning wrong dp index

4. Identify the error in this bottom-up DP code for computing factorial of n:

int factorial(int n) {
  int dp[100];
  dp[0] = 1;
  for (int i = 1; i <= n; i++) {
    dp[i] = dp[i-1] * i;
  }
  return dp[n];
}

medium

A. dp array not initialized to zero

B. Loop should start from i = 0

C. dp array size might be too small for large n

D. No error, code is correct

Solution

1. Step 1: Check dp array size

   dp array size is fixed at 100, so if n > 99, dp[i] access is out of bounds causing error.

2. Step 2: Analyze other parts

   Initialization and loop logic are correct; loop starts at 1 and dp[0] = 1 is base case.

3. Final Answer:

   dp array size might be too small for large n -> Option C

4. Quick Check:

   Array size must fit n [OK]

Hint: Ensure dp array size >= n to avoid overflow [OK]

Common Mistakes:

• Assuming dp size is always enough
• Starting loop at wrong index
• Ignoring base case initialization

5. Given the problem to find the minimum number of coins to make amount 11 using coins {1, 3, 5}, which bottom-up DP table entry dp[11] will hold after running this code?

int coins[] = {1, 3, 5};
int n = 11;
int dp[12];
dp[0] = 0;
for (int i = 1; i <= n; i++) {
  dp[i] = 1000; // large number
  for (int c : coins) {
    if (i - c >= 0) {
      dp[i] = std::min(dp[i], dp[i-c] + 1);
    }
  }
}
std::cout << dp[n];

hard

A. 6

B. 5

C. 4

D. 3

Solution

1. Step 1: Understand DP logic for coin change

   dp[i] stores minimum coins for amount i. For 11, check coins 1,3,5 and pick minimum.

2. Step 2: Calculate dp values up to 11

   dp[0]=0; dp[1]=1; dp[2]=2; dp[3]=1; dp[4]=2; dp[5]=1; dp[6]=2; dp[7]=3; dp[8]=2; dp[9]=3; dp[10]=2; dp[11]=3 (e.g., 5+5+1 or 3+3+5)

3. Final Answer:

   3 -> Option D

4. Quick Check:

   Minimum coins for 11 = 3 [OK]

Hint: Build dp from 0 to n, pick min coins at each step [OK]

Common Mistakes:

• Not initializing dp[0] = 0
• Using max int instead of large number
• Forgetting to check i - coin >= 0