Efficient Submatrix Sum Queries Using Prefix Sums

Problem Statement Given an n×m integer matrix and q queries, each query specifies the coordinates of the top-left and bottom-right corners of a submatrix. For each query, compute the sum of all elements within the specified submatrix. Solution Approach The problem can be efficiently solved using 2D prefix sums. By precomputing the cumulative su ...

Posted on Sun, 28 Jun 2026 17:36:37 +0000 by trufla

Efficient Range Queries and Updates: Prefix Sums and Difference Arrays

1. Prefix Sum Technique 1.1 One-Dimensional Prefix Sum The prefix sum algorithm is an optimization technique used to calculate the sum of elements within a specific range $[L, R]$ in $O(1)$ time after an $O(N)$ preprocessing step. In a naive approach, calculating range sums repeatedly would result in $O(N \times M)$ complexity for $M$ queries; ...

Posted on Sun, 21 Jun 2026 17:52:00 +0000 by musicbase

Calculating Minimum Operations to Transform Array Elements to Target Values

Problem Overview Given a positive integer array nums and m queries, each query asks for the minimum number of operations to transform all elements in nums to a target value q. One operation allows incrementing or decrementing a single element by 1. The array resets to its original state after each query. Solution Approach For each target value, ...

Posted on Sat, 13 Jun 2026 17:08:03 +0000 by Ramtree

Maximum Bitwise OR After K Doubling Operations

Problem Statement Given a integer array nums of length n (0-indexed) and an integer k, you may perform the following operation at most k times: Select any element and multiply it by 2 Return the maximum value of nums[0] | nums[1] | ... | nums[n - 1], where a | b denotes the bitwise OR operation. Examples: Example 1: Input: nums = [12,9], k = ...

Posted on Mon, 18 May 2026 21:53:55 +0000 by opels