Algorithms: Techniques and Theory

(CS 388G)

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Modern computational applications often involve massive data sets. In this setting, it is crucial to employ asymptotically efficient algorithms. This course presents techniques for the design and analysis of polynomial-time algorithms. Unfortunately, many optimization problems that arise in practice are unlikely to be polynomial-time solvable. This course presents techniques for establishing evidence of such computational intractability, especially NP-hardness. Even if a given optimization problem is NP-hard, it may be possible to compute near-optimal solutions efficiently. This course presents techniques for the design and analysis of efficient approximation algorithms.

Topics include growth of functions, divide-and-conquer algorithms, dynamic programming, greedy algorithms, basic graph algorithms, network flow, minimum-cost matching, linear program-ming, randomized algorithms, data structures (hashing, amortized analysis, splay trees, union-find, and Fibonacci heaps), online algorithms for paging, P, NP, NP-completeness, and approximation algorithms.

  • What You Will Learn

    • Techniques for the design and analysis of efficient algorithms
    • Techniques for establishing evidence of computational intractability
    • Techniques for coping with computational intractability


    • Growth of functions, divide-and-conquer algorithms (1 week)
    • Dynamic programming (0.5 weeks)
    • Greedy algorithms (1 week)
    • Basic graph algorithms (0.5 weeks)
    • Network flow algorithms (1.5 weeks)
    • Minimum-cost matching (0.5 weeks)
    • Linear programming (0.5 weeks)
    • Randomized algorithms (0.5 weeks)
    • Hashing (1 week)
    • Amortized analysis, splay trees, union-find, Fibonacci heaps (1.5 weeks)
    • Online algorithms (0.5 weeks)
    • P, NP, NP-completeness (1.5 weeks)
    • Approximation algorithms (1.5 weeks)


  • Estimated Effort

    • 10-12 hours/week

    Course Category

    • Theory Course

    Course Availability

    • Spring 2022

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