Minimum Window Substring in DSA Javascript - Time & Space Complexity

Time Complexity: Minimum Window Substring

O(n + m)

Understanding Time Complexity

We want to know how the time needed to find the smallest substring containing all characters grows as the input string gets longer.

How does the algorithm's work increase when the input size grows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

function minWindow(s, t) {
  const need = new Map();
  for (const c of t) need.set(c, (need.get(c) || 0) + 1);
  let left = 0, right = 0, missing = t.length, start = 0, minLen = Infinity;

  while (right < s.length) {
    if (need.has(s[right])) {
      if (need.get(s[right]) > 0) missing--;
      need.set(s[right], need.get(s[right]) - 1);
    }
    right++;

    while (missing === 0) {
      if (right - left < minLen) {
        start = left;
        minLen = right - left;
      }
      if (need.has(s[left])) {
        need.set(s[left], need.get(s[left]) + 1);
        if (need.get(s[left]) > 0) missing++;
      }
      left++;
    }
  }
  return minLen === Infinity ? "" : s.substring(start, start + minLen);
}
    

This code finds the smallest substring in s that contains all characters of t.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The while loop moving the right pointer through string s, and the inner while loop moving the left pointer to shrink the window.
• How many times: Each character in s is visited at most twice, once when right moves forward and once when left moves forward.

How Execution Grows With Input

As the input string s grows, the algorithm scans through it with two pointers that each move forward without going back.

Input Size (n)	Approx. Operations
10	About 20 moves of pointers
100	About 200 moves of pointers
1000	About 2000 moves of pointers

Pattern observation: The number of operations grows roughly twice the input size, so it grows linearly.

Final Time Complexity

Time Complexity: O(n + m)

This means the time grows linearly with the size of both input strings s and t.

Common Mistake

[X] Wrong: "The nested while loops make this algorithm quadratic, so it is O(n²)."

[OK] Correct: Each pointer only moves forward through s once, so total work is linear, not quadratic.

Interview Connect

Understanding how two-pointer sliding window techniques work efficiently is a valuable skill for many string problems in interviews.

Self-Check

"What if we changed the algorithm to reset the left pointer back to zero each time we find a valid window? How would the time complexity change?"

Practice

1. What does the "Minimum Window Substring" problem ask you to find given two strings s and t?

easy

A. The number of times t appears in s

B. The longest substring in s that does not contain any character from t

C. The smallest substring in s that contains all characters of t

D. The substring of s that is exactly equal to t

Solution

1. Step 1: Understand the problem goal

   The problem asks to find the smallest part of string s that has all characters from string t, including duplicates.

2. Step 2: Compare options with the problem statement

   The smallest substring in s that contains all characters of t matches this goal exactly. Other options describe different problems.

3. Final Answer:

   The smallest substring in s that contains all characters of t -> Option C

4. Quick Check:

   Minimum Window Substring = smallest substring with all chars [OK]

Hint: Look for smallest substring covering all chars of target [OK]

Common Mistakes:

• Confusing longest substring with smallest
• Ignoring duplicate characters in t
• Thinking it counts occurrences instead of substring

2. Which of the following JavaScript code snippets correctly initializes a frequency map for characters in string t?

easy

A. const freq = {}; for (const char of t) { freq[char] = (freq[char] || 0) + 1; }

B. const freq = new Map(); for (const char of t) { freq.set(char, freq.get(char) + 1); }

C. const freq = {}; for (let i = 0; i < t.length; i++) { freq[t[i]] = freq[t[i]] + 1; }

D. const freq = []; for (const char of t) { freq[char] += 1; }

Solution

1. Step 1: Check initialization and increment logic

   const freq = {}; for (const char of t) { freq[char] = (freq[char] || 0) + 1; } initializes an empty object and increments counts safely using freq[char] = (freq[char] || 0) + 1, which works even if freq[char] is undefined.

2. Step 2: Identify errors in other options

   const freq = new Map(); for (const char of t) { freq.set(char, freq.get(char) + 1); } tries to use Map but calls freq.get(char) + 1 without checking if freq.get(char) is undefined, causing NaN. const freq = {}; for (let i = 0; i < t.length; i++) { freq[t[i]] = freq[t[i]] + 1; } tries to add 1 to undefined without default. const freq = []; for (const char of t) { freq[char] += 1; } uses an array but accesses with string keys, which is invalid.

3. Final Answer:

   const freq = {}; for (const char of t) { freq[char] = (freq[char] || 0) + 1; } -> Option A

4. Quick Check:

   Safe frequency map init = const freq = {}; for (const char of t) { freq[char] = (freq[char] || 0) + 1; } [OK]

Hint: Use (freq[char] || 0) + 1 to safely count chars [OK]

Common Mistakes:

• Not initializing counts before adding
• Using Map without checking undefined values
• Using array with string keys

3. What will be the output of the following JavaScript code snippet?

const s = "ADOBECODEBANC";
const t = "ABC";

function minWindow(s, t) {
  const need = {};
  for (const c of t) need[c] = (need[c] || 0) + 1;
  let left = 0, right = 0, count = t.length;
  let minLen = Infinity, start = 0;

  while (right < s.length) {
    if (need[s[right]] > 0) count--;
    need[s[right]] = (need[s[right]] || 0) - 1;
    right++;

    while (count === 0) {
      if (right - left < minLen) {
        minLen = right - left;
        start = left;
      }
      need[s[left]]++;
      if (need[s[left]] > 0) count++;
      left++;
    }
  }
  return minLen === Infinity ? "" : s.substring(start, start + minLen);
}

console.log(minWindow(s, t));

medium

A. "DOBECODEBA"

B. "ADOBEC"

C. "CODEBA"

D. "BANC"

Solution

1. Step 1: Understand the sliding window logic

   The code uses two pointers expanding and shrinking a window over s. It tracks needed characters from t and shrinks window when all are found.

2. Step 2: Trace the window to find minimum substring

   For s = "ADOBECODEBANC" and t = "ABC", the smallest substring containing all chars is "BANC" starting at index 9 with length 4.

3. Final Answer:

   "BANC" -> Option D

4. Quick Check:

   Minimum window substring = "BANC" [OK]

Hint: Trace sliding window to find smallest valid substring [OK]

Common Mistakes:

• Confusing substring start and end indices
• Not updating minimum length correctly
• Ignoring character counts when shrinking window

4. Identify the bug in this JavaScript function for Minimum Window Substring:

function minWindow(s, t) {
  const need = {};
  for (const c of t) need[c] = (need[c] || 0) + 1;
  let left = 0, right = 0, count = t.length;
  let minLen = Infinity, start = 0;

  while (right < s.length) {
    if (need[s[right]] > 0) count--;
    need[s[right]]--;
    right++;

    while (count === 0) {
      if (right - left < minLen) {
        minLen = right - left;
        start = left;
      }
      need[s[left]]++;
      if (need[s[left]] > 0) count++;
      left++;
    }
  }
  return minLen === Infinity ? "" : s.substring(start, start + minLen);
}

medium

A. The count variable should be initialized to 0 instead of t.length

B. The decrement of need[s[right]] should use (need[s[right]] || 0) - 1 to avoid undefined

C. The inner while loop condition should be count > 0 instead of count === 0

D. The substring returned should use s.substring(start, right) instead of s.substring(start, start + minLen)

Solution

1. Step 1: Check how need map is updated

   The code does need[s[right]]-- without ensuring need[s[right]] is defined. If s[right] is not in t, need[s[right]] is undefined, causing NaN on decrement.

2. Step 2: Fix by defaulting undefined to 0 before decrement

   Use need[s[right]] = (need[s[right]] || 0) - 1 to avoid NaN and keep counts correct.

3. Final Answer:

   The decrement of need[s[right]] should use (need[s[right]] || 0) - 1 to avoid undefined -> Option B

4. Quick Check:

   Safe decrement with default 0 avoids NaN [OK]

Hint: Always default undefined to 0 before decrementing counts [OK]

Common Mistakes:

• Not handling characters not in t
• Wrong initialization of count variable
• Incorrect substring slicing

5. Given strings s = "aabbccdd" and t = "abc", what is the minimum window substring that contains all characters of t considering duplicates in t only once?

hard

A. "abbc"

B. "abc"

C. "aabbc"

D. "bcca"

Solution

1. Step 1: Understand character requirements from t

   String t = "abc" has characters 'a', 'b', 'c' each once. So the window must contain at least one 'a', one 'b', and one 'c'.

2. Step 2: Find smallest substring in s containing 'a', 'b', 'c'

   In s = "aabbccdd", the substring "abc" starting at index 1 (characters at indices 1:'a', 2:'b', 3:'b', 4:'c') is not contiguous as "abc" is not directly present, but the smallest substring containing all characters once is actually "abc" starting at index 1 or 2. However, the substring "abc" is present starting at index 1 to 3 ("abb") is missing 'c', so the smallest substring containing all is "abc" at indices 1 to 3 is invalid. The substring "abbc" (indices 1 to 4) contains all characters and is minimal. But the substring "abc" (indices 1 to 3) is missing 'c'. The substring "abc" (indices 2 to 4) is "bbc" missing 'a'. The substring "abc" (indices 0 to 2) is "aab" missing 'c'. The substring "abc" (indices 3 to 5) is "bcc" missing 'a'. The substring "abc" (indices 1 to 4) is "abbc" which contains all. The substring "abc" (indices 1 to 3) is "abb" missing 'c'. The substring "abc" (indices 2 to 4) is "bbc" missing 'a'. The substring "abc" (indices 0 to 4) is "aabbc" which is longer. The substring "abc" (indices 1 to 4) "abbc" is the smallest substring containing all characters once. However, the option "abc" is present as "abc", but it is not a substring of s. So the correct minimal substring is "abbc".

3. Final Answer:

   "abbc" -> Option A

4. Quick Check:

   Minimum window with all chars once = "abbc" [OK]

Hint: Count chars in t once; find smallest substring covering them [OK]

Common Mistakes:

• Counting duplicates in t multiple times
• Choosing longer substrings unnecessarily
• Thinking it counts occurrences instead of substring