Mastering critical SKILLS for Coding Interviews C++: Part 1

Learn to brute force problems as a warm-up, understanding its pros, traps, and simple examples like password guessing, two-sum checks, and palindrome changes.

Analyze algorithm complexity by evaluating time and memory, with time complexity emphasized in interviews. Recursion adds auxiliary space on the stack, while iterative methods use constant space.

Tackle the two sum problem by exploring brute force, then optimize with a hash table to achieve O(n) time, while considering edge cases like duplicates and index validity.

Identify array problem patterns across range problems, two pointers, and ad hoc cases, using prefix sums, sliding windows, subarrays, substrings, hash tables, and min and binary search to optimize solutions.

Learn how prefix sums and suffix sums enable O(1) range queries, via DP recurrence and range cancellation, with non-negative inputs enabling binary search.

Count sub-arrays with sum equal to k in an array, starting with brute-force windows and advancing to prefix-sum optimization to o(n^2). Build interview-ready strategies across language-agnostic examples.

Convert equal zeros and ones problem to a zero-sum window by treating zeros as minus ones, using a prefix-sum hash table to store earliest indices and maximize the subarray length.

Explore the fixed size sliding window technique to compute the maximum sum of K elements, contrasting with the prefix sum approach and using a running sum and boundary aware indexing.

Determine if duplicates exist within k positions in an array by using a sliding window with a hash set to track last k elements.

Master variable-size sliding windows by comparing nested-loop expansion and a single two-pointer approach to find subarrays or substrings, including maximum-sum subarrays, with efficient window expansion and validation.

Master a variable-size sliding window that expands the right end and shifts the left to keep at most K zeros, achieving an O(n) solution.

Explore the relationships between maximum and minimum subarrays, identify their possible containment or adjacency, and solve maximum absolute sum and circular subarray problems using prefix sums, duplication, and insights.

Master the two-pointers technique with left and right indices to solve sorted array problems, distinguishing it from sliding windows and achieving O(n) time and O(1) memory for target sums.

Explore solving the longest mountain subarray problem with a sliding window approach that detects a strict up sequence followed by a strict down sequence.

Solve the max area problem on an array of nonnegative integers by selecting i and j (i<j) to maximize min(A[i], A[j])*(j-i) with a two-pointer greedy shrink from the smaller side.

Extend the two-sum approach to the three-sum problem using a sorted array and two pointers to find unique triplets, while skipping duplicates and applying root cause analysis to duplicates.

Maintain the three largest and two smallest values with a min-heap and a max-heap for O(n) time and O(1) memory, and optimize brute-force using left and right max/min arrays.

Find the longest consecutive sequence in an unsorted array using a hash set. Start from numbers with no predecessor and extend to the next value for O(n) time.

Solve the missing number problem in an array from zero to n. Learn brute force, hash, sum, and xor approaches for linear time solutions.

Solve this easy string problem about anagrams by checking two strings s and t for identical character frequencies. Compare frequencies with hash tables for O(n) time or sort the strings for O(n log n), and consider canonical forms across lower and Unicode letters.

Learn isomorphic strings by enforcing bidirectional mappings with two hash tables, ensuring consistency in both directions, and compare brute-force with optimized approaches plus a canonical re-indexing method.

Explore solving the palindrome with at most one deletion by testing brute-force and recursive approaches, using left-right pointers, and a greedy observation to skip deletions when ends match.

Leverage the top-right starting point in a 2D sorted matrix to locate a target efficiently with a staircase search, achieving O(R+C) time by using both row and column order.

Explore in-place matrix rotation with O(1) memory by using permutation cycles to swap four cells at a time, comparing hard and easy approaches and highlighting cycle identification strategies.

Explore an in place matrix rotation by first transposing the matrix and then reflecting columns, using careful in place swaps to preserve data with O(1) extra memory.

Learn to solve 2D range sum queries on a matrix by building row-wise prefix sums with O(n^2) preprocessing, then answer each rectangle query in O(n) by summing row ranges.

Create a 2d image integral by applying row and column prefixes to enable constant-time rectangle sums, and note update rectangle challenges with 2d segment trees or 2d binary indexed trees.

Fix column pairs, compress between them into a 1d array with row sums, and solve non-empty 2d maximum sub-rectangle sum via the 1d maximum subarray approach in O(n^3), handling negatives.

Master the sparse matrix multiplication with a sparse-aware optimization that reorders loops and skips zeros, reducing from n^3 to near n^2 in large matrices.

Design a memory-efficient sparse matrix data structure to support incremental updates and multiplication, selecting representations based on operation frequency, and implement with simple test cases.

Design a sparse matrix using a double-view data structure with two hash-table layers for row and column entries. Implement add_cell and multiply to enable flexible transpose.

Learn to compute the largest histogram rectangle using left and right smaller indices (next smaller, previous smaller) and compare four strategies (prefix, sliding window, two-pointer, ad-hoc).

This lecture shows solving the maximal rectangle with column rearrangements by greedy sorting of per-row heights, then extends to maximal squares, flipping ones and zeros, plus editorial dp insights.

Analyze function log sequences to compute per-task execution times, excluding sub-call durations, using a stack-based approach for recursion. Parse logs into id, start, end events and accumulate timing with push-pop.

Master a stack-based approach to the longest valid parentheses substring, using an integer stack to link open expressions to their parents and accumulate lengths.