Coursera Design and Analysis of Algorithms1&2

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Coursera - Design and Analysis of Algorithms1+2

Coursera - Design and Analysis of Algorithms1+2
English | Size: 2.28 GB
Category: Misc


Part I
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Course Syllabus

Week 1: Introduction. Asymptotic analysis including big-oh notation. Divide-and-conquer algorithms for sorting, counting inversions, matrix multiplication, and closest pair.

Week 2: Running time analysis of divide-and-conquer algorithms. The master method. Introduction to randomized algorithms, with a probability review. QuickSort.

Week 3: More on randomized algorithms and probability. Computing the median in linear time. A randomized algorithm for the minimum graph cut problem.

Week 4: Graph primitives. Depth- and breadth-first search. Connected components in undirected graphs. Topological sort in directed acyclic graphs. Strongly connected components in directed graphs.

Week 5: Dijkstra's shortest-path algorithm. Introduction to data structures. Heaps and applications.

Week 6: Further data structures. Hash tables and applications. Balanced binary search trees.

Part II
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Course Syllabus

Weeks 1 and 2: The greedy algorithm design paradigm. Applications to optimal caching and scheduling. Minimum spanning trees and applications to clustering. The union-find data structure. Optimal data compression.

Weeks 3 and 4: The dynamic programming design paradigm. Applications to the knapsack problem, sequence alignment, shortest-path routing, and optimal search trees.

Weeks 5 and 6: Intractable problems and what to do about them. NP-completeness and the P vs. NP question. Solvable special cases. Heuristics with provable performance guarantees. Local search. Exponential-time algorithms that beat brute-force search.

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Tags: Algorithms, Analysis, Design, Coursera
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