Longest Increasing Subsequence in DSA C# - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Longest Increasing Subsequence (LIS) in a sequence of numbers?

The Longest Increasing Subsequence is the longest sequence of numbers from the original sequence where each number is strictly greater than the previous one, but the numbers do not need to be consecutive in the original sequence.

intermediate

What is the time complexity of the classic dynamic programming approach to find LIS?

The classic dynamic programming approach to find LIS has a time complexity of O(n²), where n is the length of the input sequence.

intermediate

In LIS problem, what does the DP array represent in the dynamic programming solution?

In the DP solution, the DP array stores the length of the longest increasing subsequence ending at each index of the input sequence.

advanced

What is the purpose of binary search in the optimized O(n log n) LIS algorithm?

Binary search is used to find the position to replace or extend the current subsequence in a helper array that keeps track of the smallest possible tail values for increasing subsequences of different lengths.

beginner

True or False: The elements of the Longest Increasing Subsequence must be contiguous in the original sequence.

False. The elements of the LIS do not need to be contiguous; they only need to be in increasing order and maintain their original relative order.

What does the Longest Increasing Subsequence represent?

A The longest sequence of equal numbers

B The longest sequence of consecutive numbers in the original sequence

C The longest sequence of decreasing numbers

D The longest sequence of strictly increasing numbers from the original sequence, not necessarily contiguous

LIS is the longest strictly increasing sequence that can be formed from the original sequence without requiring the elements to be next to each other.

What is the time complexity of the optimized LIS algorithm using binary search?

A O(n log n)

B O(n²)

C O(log n)

D O(n)

The optimized LIS algorithm uses binary search to maintain a helper array, achieving O(n log n) time complexity.

In the DP approach, what does dp[i] represent?

A Maximum number in the sequence

B Length of LIS starting at index i

C Length of LIS ending at index i

D Index of the smallest element

dp[i] stores the length of the longest increasing subsequence that ends exactly at index i.

Which data structure is commonly used to optimize LIS to O(n log n)?

A Array with binary search

B Queue

C Stack

D Linked list

An array combined with binary search is used to efficiently track the smallest tail elements of increasing subsequences.

Can the LIS contain elements that are not next to each other in the original sequence?

A Only if the sequence is sorted

B Yes

C Only if they are consecutive numbers

D No

LIS elements maintain the original order but do not need to be contiguous.

Explain how the dynamic programming approach finds the Longest Increasing Subsequence.

Think about building solutions for smaller parts and combining them.

Describe the role of binary search in the optimized LIS algorithm and how it improves performance.

Focus on how searching helps avoid repeated comparisons.

Practice

1. What does the Longest Increasing Subsequence (LIS) problem ask you to find in a sequence of numbers?

easy

A. The shortest subsequence with increasing numbers

B. The longest subsequence where each number is smaller than the previous one

C. The longest subsequence where numbers are equal

D. The longest subsequence where each number is larger than the previous one

Solution

1. Step 1: Understand the problem definition

   The LIS problem looks for a subsequence where every next number is bigger than the one before it.

2. Step 2: Compare options with the definition

   Only The longest subsequence where each number is larger than the previous one matches this definition exactly.

3. Final Answer:

   The longest subsequence where each number is larger than the previous one -> Option D

4. Quick Check:

   LIS = increasing subsequence [OK]

Hint: LIS means strictly increasing numbers in order [OK]

Common Mistakes:

• Confusing increasing with decreasing subsequence
• Thinking LIS means contiguous elements
• Assuming equal numbers count as increasing

2. Which of the following C# code snippets correctly initializes an array to find LIS length?

easy

A. int[] dp = new int[n]; for(int i=0; i

B. int dp[] = new int[n]; for(int i=0; i

C. int[] dp = new int[n]; for(int i=0; i<=n; i++) dp[i] = 1;

D. int[] dp = new int[n+1]; for(int i=1; i<=n; i++) dp[i] = 1;

Solution

1. Step 1: Check array initialization syntax

   int[] dp = new int[n]; for(int i=0; i

2. Step 2: Identify errors in other options

   int dp[] = new int[n]; for(int i=0; i

3. Final Answer:

   int[] dp = new int[n]; for(int i=0; i -> Option A

4. Quick Check:

   Correct array init and loop range [OK]

Hint: Use zero-based indexing and initialize dp with 1 [OK]

Common Mistakes:

• Using wrong loop bounds causing index errors
• Initializing dp elements to 0 instead of 1
• Incorrect array declaration syntax

3. What is the output of the following C# code snippet?

int[] arr = {10, 22, 9, 33, 21, 50, 41, 60};
int n = arr.Length;
int[] dp = new int[n];
for (int i = 0; i < n; i++) dp[i] = 1;
for (int i = 1; i < n; i++) {
  for (int j = 0; j < i; j++) {
    if (arr[i] > arr[j] && dp[i] < dp[j] + 1) {
      dp[i] = dp[j] + 1;
    }
  }
}
int max = 0;
for (int i = 0; i < n; i++) {
  if (dp[i] > max) max = dp[i];
}
Console.WriteLine(max);

medium

A. 5

B. 4

C. 6

D. 3

Solution

1. Step 1: Trace dp array updates for LIS length

   Initial dp = [1,1,1,1,1,1,1,1]. Update dp as we find increasing subsequences: - i=1: arr[1]=22 > arr[0]=10, dp[1]=max(1, dp[0]+1)=2 - i=2: arr[2]=9 no update - i=3: arr[3]=33 > 22, dp[3]=max(1, dp[1]+1)=3 - i=4: arr[4]=21 > 10, dp[4]=2; also 21 < 22 no update - i=5: arr[5]=50 > 33, dp[5]=max(1, dp[3]+1)=4 - i=6: arr[6]=41 > 33, dp[6]=max(1, dp[3]+1)=4; also 41 > 21 dp[4]+1=3 no update - i=7: arr[7]=60 > 50 dp[5]+1=5, dp[7]=5 Final dp = [1,2,1,3,2,4,4,5]

2. Step 2: Find max value in dp

   Max dp value is 5, which is the length of the LIS.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   LIS length = 5 [OK]

Hint: Track dp updates for each element comparing previous smaller elements [OK]

Common Mistakes:

• Not updating dp[i] correctly inside nested loops
• Confusing max dp value with sum of elements
• Off-by-one errors in loops

4. Identify the bug in this LIS code snippet and choose the correct fix:

int[] arr = {3, 10, 2, 1, 20};
int n = arr.Length;
int[] dp = new int[n];
for (int i = 0; i <= n; i++) dp[i] = 1;  // Bug here
for (int i = 1; i < n; i++) {
  for (int j = 0; j < i; j++) {
    if (arr[i] > arr[j] && dp[i] < dp[j] + 1) {
      dp[i] = dp[j] + 1;
    }
  }
}
int max = dp[0];
for (int i = 1; i < n; i++) {
  if (dp[i] > max) max = dp[i];
}
Console.WriteLine(max);

medium

A. Remove the initialization loop entirely

B. Change 'i <= n' to 'i < n' in the initialization loop

C. Change inner loop to 'for (int j = 1; j < i; j++)'

D. Initialize dp array with zeros instead of ones

Solution

1. Step 1: Identify the cause of runtime error

   The loop 'for (int i = 0; i <= n; i++)' tries to access dp[n], which is out of bounds since dp size is n (0 to n-1).

2. Step 2: Fix the loop condition

   Changing the loop condition to 'i < n' prevents out-of-range access and correctly initializes all dp elements to 1.

3. Final Answer:

   Change 'i <= n' to 'i < n' in the initialization loop -> Option B

4. Quick Check:

   Loop bounds must match array size [OK]

Hint: Check loop bounds carefully to avoid index errors [OK]

Common Mistakes:

• Using <= instead of < in loops over arrays
• Initializing dp with zeros causing wrong LIS length
• Changing inner loop start incorrectly

5. Given the sequence {5, 7, 4, 8, 6, 9}, what is the length of the Longest Increasing Subsequence if you must also skip any number that is even?

hard

A. 4

B. 2

C. 3

D. 1

Solution

1. Step 1: Filter out even numbers from the sequence

   Original: 5, 7, 4, 8, 6, 9 Remove even: 5, 7, 9

2. Step 2: Find LIS length on filtered sequence

   Sequence is 5, 7, 9 which is strictly increasing. Length = 3. But question says "must also skip any number that is even" so 4,8,6 are removed. However, 7 is odd, so sequence is 5,7,9.

3. Step 3: Re-examine options and question carefully

   Wait, the question says "must also skip any number that is even" meaning we cannot include even numbers in LIS. So LIS is on 5,7,9 with length 3. But options do not have 3 as correct answer except 3. Check if 7 is odd? Yes. So LIS length is 3.

4. Final Answer:

   3 -> Option C

5. Quick Check:

   Filter evens, LIS length = 3 [OK]

Hint: Filter out evens first, then find LIS length [OK]

Common Mistakes:

• Including even numbers in LIS calculation
• Confusing subsequence with substring
• Miscounting length after filtering