The article first reflects on ByteDance's expanding English testing policy, then presents LeetCode problem 799 “Champagne Tower”, describing its mechanics and offering a linear‑DP solution with full implementations in Java, C++, Python and TypeScript, along with complexity analysis.
The article first shares a personal anecdote about job background checks and then presents a detailed explanation of LeetCode problem 5 – Longest Palindromic Substring – including problem description, dynamic‑programming analysis, recurrence formula, and complete Java and C++ code examples.
This article explains how to compute the maximum number of books that can be stacked without rotation by converting the problem into a longest increasing subsequence (LIS) task, sorting books by length and width, applying a dynamic‑programming DP approach, and analyzing its time and space complexities.
The article presents a dynamic‑programming solution to compute the maximum sum of a contiguous subarray when you may replace at most one element with a given value, detailing problem constraints, input/output format, DP state definitions, transition formulas, reference Python code, and complexity analysis.
This article explains the dynamic‑programming solution to count the number of distinct ways a monkey can climb a staircase of n steps when each jump can be either 1 or 3 steps, including problem definition, recurrence relation, Python implementation, and time‑space analysis.
This article explains the classic House Robber interview question, detailing the problem constraints, illustrating why a naive alternating‑house approach fails, and presenting a clear dynamic‑programming solution with recurrence, base cases, and a concise code example.
This article introduces dynamic programming as an optimization technique from operations research, explains its fundamental concepts, optimality principle, no‑after‑effect property, and overlapping subproblems, outlines the typical problem‑model steps, and demonstrates a practical C++ implementation for solving the fractional knapsack problem.
This article explains the necessity of Android screen adaptation due to system and device fragmentation, defines screen size, resolution, and density concepts, introduces density‑independent units (dp, sp), and provides practical layout, component, and image resource adaptation techniques with code examples.
