Longest Increasing Subsequence in DSA Javascript - Time & Space Complexity

Time Complexity: Longest Increasing Subsequence

O(n²)

Understanding Time Complexity

We want to understand how the time needed to find the longest increasing subsequence changes as the input list grows.

How does the number of steps grow when the list gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

function lengthOfLIS(nums) {
  const n = nums.length;
  const dp = new Array(n).fill(1);
  for (let i = 1; i < n; i++) {
    for (let j = 0; j < i; j++) {
      if (nums[j] < nums[i]) {
        dp[i] = Math.max(dp[i], dp[j] + 1);
      }
    }
  }
  return Math.max(...dp);
}
    

This code finds the length of the longest increasing subsequence by checking all pairs of elements.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Nested loops comparing pairs of elements.
• How many times: Outer loop runs n times, inner loop runs up to i times, so roughly n x n = n² times.

How Execution Grows With Input

As the list size grows, the number of comparisons grows much faster because each new element is compared with all before it.

Input Size (n)	Approx. Operations
10	About 100 comparisons
100	About 10,000 comparisons
1000	About 1,000,000 comparisons

Pattern observation: Operations grow roughly by the square of the input size.

Final Time Complexity

Time Complexity: O(n²)

This means if the input list doubles in size, the time to run the code roughly quadruples.

Common Mistake

[X] Wrong: "The code only looks at each element once, so it must be O(n)."

[OK] Correct: Each element is compared with many others inside nested loops, so the total steps grow much faster than just the number of elements.

Interview Connect

Understanding this time complexity helps you explain why some solutions are slower and shows your skill in analyzing nested loops, a common interview topic.

Self-Check

"What if we used a binary search to find the position to update instead of checking all previous elements? How would the time complexity change?"

Practice

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

easy

A. The longest subsequence where numbers are all equal

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

C. The longest subsequence with alternating increasing and decreasing numbers

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

Solution

1. Step 1: Understand the definition of LIS and compare options

   The Longest Increasing Subsequence is a subsequence where every next number is strictly greater than the previous one. The longest subsequence where each number is larger than the previous one matches the definition exactly, others describe different patterns.

2. Final Answer:

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

3. Quick Check:

   LIS = strictly increasing subsequence [OK]

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

Common Mistakes:

• Confusing increasing with decreasing subsequence
• Thinking LIS means contiguous elements only
• Assuming equal numbers are allowed in LIS

2. Which of the following JavaScript code snippets correctly initializes an array to store LIS lengths for each element in the input array arr?

easy

A. const lis = Array(arr.length).fill(0);

B. const lis = new Array(arr.length).fill(null);

C. const lis = new Array(arr.length).fill(1);

D. const lis = new Array(arr.length).fill(undefined);

Solution

1. Step 1: Understand LIS initialization and check syntax

   Each element starts with LIS length 1 because each element alone is a subsequence. const lis = new Array(arr.length).fill(1); correctly creates an array of length arr.length filled with 1s.

2. Final Answer:

   const lis = new Array(arr.length).fill(1); -> Option C

3. Quick Check:

   Initialize LIS array with 1s [OK]

Hint: LIS array starts with 1 for each element [OK]

Common Mistakes:

• Filling with 0 instead of 1
• Using fill(null) or fill(undefined) which are incorrect for LIS
• Not matching array length to input array

3. What is the length of the Longest Increasing Subsequence for the array [10, 22, 9, 33, 21, 50, 41, 60]?

medium

A. 4

B. 5

C. 6

D. 7

Solution

1. Step 1: Identify increasing subsequences and confirm no longer exists

   Possible LIS includes [10, 22, 33, 50, 60] which has length 5. Checking other subsequences shows none longer than 5.

2. Final Answer:

   5 -> Option B

3. Quick Check:

   LIS length for given array = 5 [OK]

Hint: Trace increasing numbers, pick longest chain [OK]

Common Mistakes:

• Counting non-increasing sequences
• Confusing subsequence with subarray
• Missing longer subsequences by skipping elements

4. Consider this JavaScript code snippet to find LIS length:

function lengthOfLIS(arr) {
  const lis = new Array(arr.length).fill(1);
  for (let i = 1; i < arr.length; i++) {
    for (let j = 0; j < i; j++) {
      if (arr[i] > arr[j]) {
        lis[i] = Math.max(lis[i], lis[j] + 1);
      }
    }
  }
  return lis[arr.length];
}

What is the error in this code?

medium

A. Returns lis[arr.length] which is undefined, should return max value in lis array

B. Incorrect initialization of lis array with 1s

C. The inner loop should start from i instead of 0

D. The comparison should be arr[i] < arr[j] instead of arr[i] > arr[j]

Solution

1. Step 1: Check return statement and correct return logic

   Returning lis[arr.length] accesses out-of-bounds index (undefined). Should return the maximum value in lis array, e.g., Math.max(...lis).

2. Final Answer:

   Returns lis[arr.length] which is undefined, should return max value in lis array -> Option A

3. Quick Check:

   Return max LIS length, not out-of-bounds element [OK]

Hint: Return max value in lis array, not lis[arr.length] [OK]

Common Mistakes:

• Returning wrong index from lis array
• Misunderstanding loop ranges
• Reversing comparison operators

5. Given the array [3, 10, 2, 1, 20], which of the following code snippets correctly finds the length of the Longest Increasing Subsequence using binary search optimization?

hard

A. function lengthOfLIS(arr) {
const tails = [];
for (const num of arr) {
let left = 0, right = tails.length;
while (left < right) {
const mid = Math.floor((left + right) / 2);
if (tails[mid] < num) left = mid + 1;
else right = mid;
}
tails[left] = num;
}
return tails.length;
}

B. function lengthOfLIS(arr) {
const tails = [];
for (const num of arr) {
let left = 0, right = tails.length - 1;
while (left <= right) {
const mid = Math.floor((left + right) / 2);
if (tails[mid] <= num) left = mid + 1;
else right = mid - 1;
}
tails[left] = num;
}
return tails.length;
}

C. function lengthOfLIS(arr) {
const tails = [];
for (const num of arr) {
let left = 0, right = tails.length;
while (left < right) {
const mid = Math.ceil((left + right) / 2);
if (tails[mid] > num) right = mid - 1;
else left = mid + 1;
}
tails[left] = num;
}
return tails.length;
}

D. function lengthOfLIS(arr) {
const tails = [];
for (const num of arr) {
let left = 0, right = tails.length;
while (left < right) {
const mid = Math.floor((left + right) / 2);
if (tails[mid] > num) right = mid;
else left = mid + 1;
}
tails[left] = num;
}
return tails.length;
}

Solution

1. Step 1: Understand binary search in tails array and check boundaries/conditions

   We find the position to replace or append the current number to keep tails sorted. function lengthOfLIS(arr) { const tails = []; for (const num of arr) { let left = 0, right = tails.length; while (left < right) { const mid = Math.floor((left + right) / 2); if (tails[mid] < num) left = mid + 1; else right = mid; } tails[left] = num; } return tails.length; } uses correct boundaries and conditions: left < right, mid floor, tails[mid] < num moves left up.

2. Final Answer:

   function lengthOfLIS(arr) { const tails = []; for (const num of arr) { let left = 0, right = tails.length; while (left < right) { const mid = Math.floor((left + right) / 2); if (tails[mid] < num) left = mid + 1; else right = mid; } tails[left] = num; } return tails.length; } -> Option A

3. Quick Check:

   Binary search with left < right and tails[mid] < num [OK]

Hint: Use binary search with left < right and tails[mid] < num [OK]

Common Mistakes:

• Using wrong binary search boundaries (<= instead of <)
• Incorrect mid calculation (ceil instead of floor)
• Wrong comparison operators in binary search