Tentative Class Schedule

Monday	Wednesday
January 24Getting Started Start of classes Introductions Quick Course Overview Home Page / Syllabus / Schedule Some possible class formats Clicker set up Status Check—where are we really? Chapter 1: Introduction to Algorithms Algorithmic Problem Solving Problem types Fundamental Data Structures	January 26 Chapter 2: Analysis Fundamentals 2.1 The Analysis Framework 2.2 Asymptotic Notations: O-notation, Ω-notation, Θ-notation 2.3 Analysis of nonrecursive algorithms Worksheet on Analysis of Non-recursive Algorithms Exercises 1-5 required Exercises 6-9 available for extra credit Due in paper form at start of class, Wednesday, February 2
January 31 Appendix B: Recurrence Relations Appendix B: Recursive algorithms and recurrence relations Chapter 2: Analysis Fundamentals 2.4 Analysis of recursive algorithms 2.5 Example: the nth Fibonacci Number	February 2 Chapter 2: Analysis Fundamentals 2.6 Empirical analysis of algorithms Chapter 3: Brute Force and Exhaustive Search 3.1 Selection Sort Worksheet on Analysis of Recursive and Non-recursive Algorithms
February 7 inserChapter 3: Brute Force and Exhaustive Search 3.2 Sequential Search 3.2 Brute force string matching 3.4 Exhaustive search	February 9 Chapter 3: Brute Force and Exhaustive Search 3.5 Depth-first search 3.5 Breadth-first search Worksheet on Graph Traversals
February 14 Chapter 4: Decrease ahd Conquer 4.1 Insertion Sort Reading on Insertion Sort 4.2 Topological sorting	February 16 Loop Invariants Introduction to Loop invariants Reading: Introduction to Loop Invariants Lab: Introduction to Loop Invariants
February 21 Loop Invariants/Chapter 5: Divide and Conquer Applications of Loop Invariants Single loops Variations on the binary search	February 23 Loop Invariants/Chapter 5: Divide and Conquer Reading on Quicksort 5.2 Quicksort, Partition, and other applications of loop invariants Lab: Pictoral Loop Invariants
February 28 Chapter 5: Divide and conquer Reflections on Topics to Date Algorithmic Analysis, Big-O, Big-Θ, Big-Ω Some Algorithmic Patterns Basics of Graphs Loop Invariants for Binary Search and Partition Quicksort, Improved Quicksort, and Analysis	March 2 Test 1
March 7 Chapters5-6: Transform-and-Conquer 5.1 Mergesort Reading on Merge Sort 6.4 Heaps	March 9 Chapter 5: Transform-and-Conquer 6.4 Heaps and Heap Sort Lab on Heaps Lab on Selection/Insertion/Quick/Merge Sort 6.4 Heaps
March 14 Chapter 6: Transform-and-Conquer 6.5 Horner's Rule 6.5 Binary Exponentiation	March 16 Chapter 6: Transform-and-Conquer Trees and Search Trees 6.3 AVL Trees Time to catch up before break!
March 21 Spring Break: Monday, March 21	March 23 Spring Break: Wednesday, March 23
March 28 Section 7.3: Hashing 7.3 Open Hashing (Separate Chaining)	March 30 7.3 Closed Hashing (Open Addressing) Lab on Hashing In-class Quiz
April 4 Chapter 8: Dynamic Programming Two of the following sections Efficiency Analysis for Hash Tables 8.3 Optimal Binary Search Trees	April 6 Chapter 6: Space and Time Trade-offs Examples of Dynamic Programming, such as 8,1 Example: Edit Distance Problem In-class Quiz
April 11 Chapter 9: Greedy Techniques 8.3 Optimal Binary Search Trees, continued 9.3 Dijkstra's algorithm (single-source shortest paths)	April 13 Consequences of Numeric Data Representation Lab on Dynamic Programming and Optimal Binary Search Trees Integer Representations Floating Point Representation Consequences of Data Reresentation In-class Quiz
April 18 Consequences of Representational Error Worksheet on Consequences of Numeric Representation	April 20 Test 2
April 25 Chapter 9: Greedy Techniques Notes from solutions to Test 2 9.4 Huffman trees and codes 9.1 Prim's algorithm (minimum cost spanning tree)	April 27 Chapter 11: Limitations of Algorithm Power Chapter 11: Limitations of Algorithm Power 11.1 Lower bound arguments 11.2 Decision trees 11.3 P, NP, and NP-complete problems Worksheet on Huffman Codes, Minimum Cost Spanning Trees, and Classes P and NP
May 2 Chapter 11: Limitations of Algorithm Power Halting problem Additional Observations Concerning Numeric Error The Trapezoidal Rule	May 4 Chapter 12: Coping with Limitations of Algorithm Power 12.1 Backtracking 12.2 Branch-and-bound 12.3 Approximation algoritms Worksheet on Coping with Limitations of Computing In-class Quiz
May 9 Chapter 12: Coping with Limitations of Algorithm Power 12.4 Solving nonlinear equations Bisection method Method of false position Newton's method Worksheet on Coping with Limitations of Computing (Expanded)	May 11 Last day of class: Wednesday, May 11
May 16 Exam schedule: May 16-20	May 18

CS 415, Section 002	Sonoma State University	Spring, 2022

Algorithm Analysis
Instructor: Henry M. Walker Lecturer, Sonoma State University Professor Emeritus of Computer Science and Mathematics, Grinnell College

created 22 June 2021 developed and refined Summer 2021
For more information, please contact Henry M. Walker at walker@cs.grinnell.edu.