Dynamic Programming – Theory

Alright everyone, let’s dive into the theory behind dynamic programming! Don’t worry, it’s not as scary as it sounds. Think of it as a clever strategy for solving complex problems by breaking them down into smaller, overlapping subproblems, solving each one only once, and storing the results to avoid redundant calculations. We’ll explore the core concepts – optimal substructure and overlapping subproblems – in a way that makes this powerful technique clear and approachable.

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Dive deep into Edit Distance, focusing on the Levenshtein algorithm. Learn its theory, dynamic programming approach, and practical applications with comprehensive Python code examples for spell checkers, search, and more.

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Demystifying the Longest Increasing Subsequence (LIS) Problem

#Algorithms#Dynamic Programming#Binary Search#Python#Problem Solving#Computer Science

A deep dive into the Longest Increasing Subsequence (LIS) problem, exploring brute-force, dynamic programming (O(n^2)), and optimized O(n log n) solutions with practical Python examples.

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