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Lecture
Algorithm Analysis: Time Complexity
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Maximum Subarray Problem
Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.
Maximum Subarray Problem
Explores the Master method, the Maximum Subarray Problem, and optimal solution structures.
Online Matching in Evolving Environments
Explores online matching in evolving environments, addressing challenges and solutions for adapting algorithms to changing data.
Dynamic Programming: Palindromic Subsequences
Explores dynamic programming for palindromic subsequences, merging binary search trees, and finding the median of two sorted arrays.
Complexity & Induction: Algorithms & Proofs
Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.
Complexity of Algorithms
Explores the complexity of algorithms, including big-O notation and efficiency analysis.
Matrix Multiplication and Heaps: Efficient Algorithms
Discusses Strassen's algorithm for matrix multiplication and heaps, covering efficient algorithms and their applications in computer science.
Introduction to Algorithms
Introduces the importance of studying algorithms, presents a clever algorithm for calculating an arithmetic series, and discusses efficiency and correctness in algorithms.
Merge Sort: Divide-and-Conquer Approach
Introduces the merge sort algorithm through the divide-and-conquer approach, emphasizing correctness and time analysis.
