Local search (12.1); simulated annealing (brief) (12.2)

Randomized Max-SAT (13.4); universal hashing (13.6); perfect hashing (CLRS 11.5)

Closest point (13.7); introduction to primality testing

Primality testing (see  Cormen, Leiserson, Rivest 31.8)

Midterm solutions

Set cover finished (11.3); weighted vertex cover 11.4

Linear programming/integer programming

Approximations for: disjoint paths 11.5, 11.8 knapsack

Pspace (9.1,9.2); dealing with hard problems

10.2 Independent set; approximations: vertex cover, scheduling 11.1

Design and Analysis of Algorithms (Fall, 2008)

Hard problems: NP, decision vs. optimization, subset sum reductions

Advanced graph algorithms

Network flow applications

Finish 6.5; sequence alignment (6.6); linear space (6.7)

Linear space analysis (6.7); shortest paths (6.8-6.9, bit of 6.10)

Introduction: Types of analysis

Network Flows (7.1, 7.2): Problem definition, residual graphs, Ford-Fulkerson algorithm

Network flows: Scaling algorithm, application to bipartite matching, disjoint paths (7.3, 7.5, 7.6)

Cohesive and logical follow up to the undergraduate algorithms class. These lectures will not teach you how to program exactly, but they will scaffold how you can think about algorithms at a high level and problem solve with computers. 

Based on my experience, you&#39;ll be better equipped to evaluate and learn from the many, many resources scattered online. There&#39;s a flood of data structures and algorithms available, so it helps to know the main categories, problem types, and tools to evaluate them in practice.

Good luck and happy coding!

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1 quote, 3 ideas & 1 question from a carefully curated podcast episode.
Every Monday & Wednesday.

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