50 Days of LeetCode In Typescript: MAANG Coding Interview

Explore the valid mountain array problem: determine if an integer array of length at least three has a strict increasing prefix followed by a strict decreasing suffix.

Detect an increasing subsequence with a loop, break on a decrease, and ensure the end of the array forms a trailing decreasing subsequence, handling no increase or end-of-array cases.

This pseudocode walkthrough demonstrates scanning an array to identify an increasing subsequence, then a decreasing subsequence, validating a mountain array while analyzing O(n) time and O(1) space.

Check a valid mountain array by simulating a climber ascending while A[i] > A[i-1], then descending while A[i] < A[i-1], returning true at the end and false otherwise.

Learn how to minimize boats for the boats to save people problem by pairing weights within a limit, using at most two per boat and guaranteed solvability.

Sort the weights, then use two pointers to pair heavy and light candidates within the limit, maximizing two-person boats and minimizing total boats.

Walks through a pseudocode solution for the boat problem using a sorted two-pointer approach that pairs lightest and heaviest within the limit, revealing the three-boat result.

Sort the weights in ascending order and use two pointers from both ends to pair the lightest and heaviest people within a limit, incrementing boats and returning the minimum count.

Move all zeros to the end of an array while preserving the relative order of non-zero elements, using a brute-force TypeScript approach.

Walk through a brute force pseudocode that collects non-zero elements into an output array, appends zeros to preserve order, and analyzes time and space complexity for MAANG interview in TypeScript.

Optimize a brute force array by moving non-zero elements to the front in place using a two-pointer technique, and fill the rest with zeros.

Walk through the optimal move zeros solution in pseudocode, showing how non-zero elements are shifted forward and zeros filled, with time complexity o(n+m) and constant space.

Move zeros to the end of an in-place integer array by scanning with a non-zero index and a zero index, preserving non-zero order and using no extra space.

Explore the longest substring without repeating characters, a contiguous substring problem, with subsequences distinction, test cases including empty strings, and a simple brute force approach.

Discover how to find the longest substring without repeating characters using a brute force approach, checking substrings from each starting position, and using hash maps for quick duplicate detection.

Walk through a pseudocode brute-force solution for longest substring without repeating characters, using left and right pointers and a seen characters map. Introduce sliding window optimization to maximize length.

Master the sliding window approach to find the longest substring without repeating characters. Use left and right pointers and a last-seen index map to update the window and length.

Watch a pseudocode walkthrough of a sliding window approach that uses a map to track character indices, updates L and R, and computes the longest substring without repeats.

Implement a sliding window in TypeScript using a map to track last positions, adjust the left pointer to avoid duplicates, and return the length of the longest unique substring.

Learn to find the first and last positions of a target in a sorted array using brute force, by scanning left for the first occurrence and right for the last.

Walk through a brute force pseudocode for finding first and last indices of a target in an array, with time complexity O(n) and space complexity O(1).

This lecture explains the optimal approach to locate the first and last positions of a target in a sorted array with binary search, using left and right pointers.

Watch a pseudocode walkthrough of binary search with two pointers to find the first and last occurrences of a target in a sorted array.

Walks through a two-pointer binary search to find the last occurrence of a target in a sorted array, adjusting bounds by mid and evaluating time and space complexity.

Apply binary search to locate the first and last positions of a target in a sorted array, implementing find first and find last methods with precise boundary checks.

tackle the first bad version problem with a brute force linear scan using the is bad version method, returning the first bad index. note its O(n) time and O(1) space.

Apply binary search with isBadVersion to identify the first bad version by halving the search range at each middle, narrowing until the first bad version remains.

Demonstrates a binary search with two pointers to find the first bad version using is bad version method, mid checks, and range halving, with time O(log n) and space O(1).

Implement a binary search to locate the first bad version using left and right pointers, mid checks, and the isBadVersion test in a 1-based version sequence.

Identify the missing number in an array of n distinct numbers from zero to n and return it.

Apply the brute force approach: sort the input and compare consecutive elements to find a gap greater than one, returning the missing number between zero and five via a loop.

Explore approaches to find the missing number in a zero-to-n array of distinct values, and convert a quadratic search into linear time using a hash map for constant-time lookups.

Walks through pseudocode for approach 2 using a present numbers map to mark input values and find the missing number from 0 to n, with O(n) time and O(n) space.

Implement a function to find the missing number in an integer array using sums. Subtract the current sum from the intended sum (0 to n) to reveal the missing value.

uncover the optimal approach to find a missing number using Gauss's formula for 0 to n. compare the expected and actual sums in linear time with constant space.

Implement the missing number function by tallying the input, compute the expected total with Gauss's formula n(n+1)/2, then subtract the current sum to find the missing number.

Learn to count primes less than n with a brute force approach: define primes, handle non-negative input, and test divisibility from 2 to i-1 to tally primes.

Walk through a brute-force primality check with a pseudocode walkthrough, showing nested loops and divisibility tests, counting primes smaller than the input, and analyzing O(n^2) time with O(1) space.

Apply the sieve toothiness algorithm to identify primes up to n by marking multiples of each prime starting from i squared, up to sqrt(n), and count the remaining true values.

Explore a pseudocode walkthrough of the sieve of Eratosthenes in typescript to find primes up to 34, marking multiples false and stopping at the square root.

Implement a TypeScript function to count primes up to n using a sieve of Eratosthenes, with an isPrime array, base case n < 2, and sqrt(n) optimization.

Identify the single number in an unsorted array by counting occurrences with a hash map, then locate the element that occurs only once.

Walk through a brute‑force approach that uses a map to count element occurrences, then return the key with a single occurrence, noting time and space complexity as O(n).

Apply a better approach using a set to sum unique elements and subtract the actual sum, yielding twice the unique sum minus the total sum in O(n) time and space.

Implement a two-pass approach in TypeScript using a map to count occurrences, then locate the value with count one and return it.

Apply the optimal approach that uses xor to cancel duplicates, achieving linear time with constant space. Explain how binary numbers and bitwise manipulation enable this solution.

Implement the optimal TypeScript approach using bitwise xor to find the single number in an array where all others appear twice, with simple code.