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Page 559 of 2109
Corporate Flight Bookings in Python
The Corporate Flight Bookings problem involves calculating the total number of seats booked on each flight when given multiple booking ranges. Each booking specifies a range of flights and the number of seats to book across that range. Problem Understanding Given n flights labeled 1 to n, and a list of bookings where each booking [i, j, k] means k seats are booked from flight i to flight j (inclusive), we need to find the total seats booked on each flight. Example With bookings [[1, 2, 10], [2, 3, 20], [2, 5, 25]] and n = ...
Read MoreCar Pooling in Python
Car pooling is a common algorithmic problem where we need to determine if a vehicle can accommodate all passenger trips without exceeding its capacity. The vehicle travels only eastward, picking up and dropping off passengers at specific locations. Problem Understanding Given a list of trips where each trip contains [num_passengers, start_location, end_location] and a vehicle capacity, we need to check if all trips can be completed without exceeding the capacity limit. Algorithm Approach We use a difference array technique to track passenger changes at each location ? Create an array to track passenger count ...
Read MoreLargest Values From Labels in Python
The Largest Values From Labels problem involves selecting items from a collection to maximize the sum while respecting constraints on the total number of items and usage limits per label. Problem Statement Given a set of items where the i-th item has values[i] and labels[i], we need to find a subset S such that: |S| ≤ num_wanted For every label L, the number of items in S with label L is ≤ use_limit The goal is to find the largest possible sum of the subset S. ...
Read MoreLetter Tile Possibilities in Python
Given a set of letter tiles, we need to find the number of possible non-empty sequences we can make using these tiles. For example, with tiles "AAB", we can form 8 different sequences: "A", "B", "AA", "AB", "BA", "AAB", "ABA", "BAA". This problem uses backtracking with frequency counting to generate all possible permutations while avoiding duplicates. Algorithm Approach We use a depth-first search (DFS) approach with the following steps: Count the frequency of each letter in the input tiles For each recursive call, try using each available letter Backtrack by restoring the letter count after ...
Read MoreFlip Columns For Maximum Number of Equal Rows in Python
Given a binary matrix of 0s and 1s, we can flip any columns to maximize the number of rows with equal values. Flipping a column changes all 0s to 1s and all 1s to 0s in that column. The goal is to find the maximum number of rows that can have identical values after optimal column flips. Original Matrix: 0 0 0 0 ...
Read MoreDistant Barcodes in Python
In a warehouse with a row of barcodes, we need to rearrange them so that no two adjacent barcodes are the same. For example, if we have [1, 1, 1, 2, 2, 2], we want to output [2, 1, 2, 1, 2, 1]. The strategy is to place the most frequent barcode first at even positions (0, 2, 4...), then fill odd positions with remaining barcodes. Algorithm Steps Count frequency of each barcode Sort barcodes by frequency (ascending order) Place the most frequent barcode at even positions (0, 2, 4...) Place remaining barcodes at odd positions ...
Read MorePrevious Permutation With One Swap in Python
Finding the previous permutation with one swap means finding the lexicographically largest permutation that is smaller than the given array by swapping exactly two elements. If no such permutation exists, we return the original array. For example, given [3, 2, 1], we can swap positions 1 and 2 to get [3, 1, 2], which is the largest permutation smaller than the original. Algorithm The algorithm follows these steps ? Find the rightmost position where A[left] > A[left + 1] If no such position exists, return the original array Find the largest element to the right ...
Read MorePartition Array for Maximum Sum in Python
The Partition Array for Maximum Sum problem involves partitioning an array into contiguous subarrays of length at most K, where each subarray's values are replaced with the maximum value in that subarray. The goal is to maximize the total sum after partitioning. For example, with array [1, 15, 7, 9, 2, 5, 10] and K=3, the optimal partitioning creates subarrays [1, 15, 7], [9], and [2, 5, 10], which become [15, 15, 15], [9], and [10, 10, 10], giving sum 84. Algorithm Approach We use dynamic programming where dp[i] represents the maximum sum for the subarray ending ...
Read MoreSmallest Integer Divisible by K in Python
Given a positive integer K, we need to find the smallest positive integer N such that N is divisible by K and N contains only the digit 1 (like 1, 11, 111, 1111, etc.). We return the length of such number N, or -1 if no such number exists. For example, if K = 3, the smallest number containing only 1s that is divisible by 3 is 111, so we return 3 (the length). Algorithm To solve this problem, we follow these steps: If K is even or divisible by 5, return -1 (no solution ...
Read MoreClumsy Factorial in Python
The clumsy factorial is a variation of the traditional factorial where we use a rotating pattern of operations: multiply (*), divide (/), add (+), and subtract (-) instead of just multiplication. For example, clumsy(10) = 10 * 9 / 8 + 7 - 6 * 5 / 4 + 3 - 2 * 1. The operations follow normal arithmetic precedence rules: multiplication and division are performed before addition and subtraction, and operations of equal precedence are evaluated left to right. We use floor division to ensure integer results. Understanding the Pattern Let's trace through clumsy(10) step by ...
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