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Lecture
Counting Sort: Decision Tree Analysis
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Related lectures (24)
Algorithms in Computer Science: Search and Sort Techniques
Provides an overview of essential search and sort algorithms in computer science.
Merge Sort: Sorting Algorithm
Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.
Optimization Algorithms: Greedy Approach
Explores optimization problems and greedy algorithms for efficient decision-making.
Introduction to Algorithms: Course Overview and Basics
Introduces the CS-250 Algorithms course, covering its structure, objectives, and key topics in algorithmic problem-solving.
Hashing and Sorting
Covers hashing, sorting, extendible hashing, linear hashing, and external sorting.
Sorting Algorithms: Sorting Methods and Comparison
Explores sorting methods, insertion sort, and algorithm comparison for efficient data organization.
Sorting Algorithms: Selection and Insertion
Introduces selection and insertion sorting algorithms, explaining their correctness and time complexity.
Insertion Sort: Basics and Analysis
Introduces Insertion Sort, explaining its basics, insertion process, and correctness analysis.
Merge Sort: Divide and Conquer
Introduces growth of functions, sorting problem, insertion sort, computational model, and merge sort.
Recursive Sorting: Merge Sort
Explains recursive sorting using Merge Sort and its linearithmic complexity.
Matrix Multiplication and Divide-and-Conquer Techniques
Discusses matrix multiplication using divide-and-conquer techniques and introduces Strassen's algorithm for improved efficiency.
Quick Sort: Divide-and-Conquer
Explores the Quick Sort algorithm, focusing on its divide-and-conquer approach and time complexity analysis.
Analysis of Algorithms
Covers the analysis of algorithms, focusing on insertion sort and computational models.
Complexity & Induction: Algorithms & Proofs
Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.
Merge Sort: Divide and Conquer
Explores the Merge Sort algorithm, applying the Divide and Conquer approach to sorting arrays efficiently.
Merge Sort: Divide-and-Conquer Approach
Introduces the merge sort algorithm through the divide-and-conquer approach, emphasizing correctness and time analysis.
Derivatives, O-Notation
Explores derivatives, O-Notation, extrema, and algorithm complexity in Analysis 1.
Analysis of Randomized Quick Sort
Analyzes the running time and comparisons in randomized quick sort, proving its efficiency and optimality in comparison sorting.
Hashing and Quick Sort
Covers the efficient implementation of hash tables and the Quick Sort algorithm.
Algorithm Design: Divide and Conquer
Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.
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