Notes and Slides - CS 165 - Project in Algorithms and Data Structures

The following documents outline the notes for the course CS 165 Project in Algorithms and Data Structures.

Notes by Lecture Schedule

• Week 1: Introduction. Algorithm Analysis. Select Sorting Algorithms.
  • Algorithm Analysis (Powerpoint slides)
  • Selected Sorting Algorithms (Powerpoint slides)
  • Microbenchmarking (PDF)
  • plotting.ipynb example
  • Shell sort example (PDF)
    • Big-Oh notation
    • Insertion sort algorithm
    • Shellsort algorithm
    • Arxiv article about Tim-sort algorithm (see Algorithm 3)
    • log-log plots (focus especially on "Relation with monomials")
    • Paper on adaptive sorting (Skip sort is described in Section 2.5)
• Week 2: Random Numbers and Random Permutations.
• Random and Pseudo-Random Numbers (Powerpoint slides)
• Random Permutations (Powerpoint slides)
• Preparing a Benchmark Driver
• Collecting Timing Data
• Processing Data with Python
• Plotting Your Data
• More about plotting data (PDF)
  • Random number generators
  • Hardware random number generators
  • Pseudorandom number generators
  • Mersenne Twister PRNG
  • Inverse transform sampling
  • Fisher-Yates random shuffle algorithm
• Week 3: Experimental analysis and binary search trees.
• Sorting Analysis for Project 1 (PDF), where the slides are from a previous year but the analysis techniques still apply
• Zip-zip trees (Lecture slides)
• Augmented search trees (Lecture slides)
  • Video of Bob Tarjan speaking about zip trees
  • Paper about zip-zip trees (PDF)
  • Paper about zip trees (PDF)
• Week 4: Bin packing.
• Bin Packing (Powerpoint slides)
• Wikipedia article on the bin-packing problem
• Approximation Algorithms for Bin Packing
• Week 5: Algorithms and Architectures.
• Algorithms and Architectures (Powerpoint slides)
• Discussion slides on Floating Point (PDF)
• Relevant Wikipedia articles:
  • Turing machine
  • Turing completeness
  • Alan Turing
  • ENIAC
  • von Neumann architecture
  • John von Neumann
  • Grace Hopper
  • Moore's law
  • Word RAM
  • External-memory algorithm
  • PRAM model
  • Parallel external memory
• Week 6: Network structure.
• Network Structure (Powerpoint slides)
• Kevin Bacon Visa commercial illustrating six degrees-of-separation
• Relevant Wikipedia articles:
  • degree distribution
  • diameter
  • clustering coefficient
• Week 7: Network algorithms.
• Network Algorithms (Powerpoint slides)
• Relevant Wikipedia articles:
  • semi-log plots, e.g., note how the log x function looks like a straight line in a lin-log plot.
  • breadth-first search
  • graph degeneracy
• Week 8: Network models.
• Network Models (Powerpoint slides)
• Required reading:
  • Efficient generation of large random networks, by Batagelj and Brandes.
• Relevant Wikipedia articles:
  • Erdős-Rényi model
  • Barabási-Albert model.
• Week 9: Navigation and propagation in networks.
• Navigation and propagation in networks (Powerpoint slides)
• Relevant Wikipedia articles:
  • network hubs
  • Kleinberg's model
  • PageRank
  • scale-free networks
• Week 10: Wrap-up on experimental algorithms.
• Principles for Experimental Algorithmics (Powerpoint slides)
• Required reading:
  • A Theoretician's Guide to the Experimental Analysis of Algorithms, by David Johnson
• Recommended reading:
  • Towards a Discipline of Experimental Algorithmics, by Bernard Moret