Syllabus
CS 427 Algorithm
Analysis
Fall 2015
Meeting
Times 
Lect: 1:00  1:50
M, Tu, Wed, Th, HB 112 
Instructor 
Dr.
Razvan Andonie, HB 214E, Office
hours 
TA 

Text 
Anany Levitin: Introduction
to The Design & Analysis of Algorithms, Addison Wesley, 2012,
Third Edition. 
This course is a study of algorithm design, algorithm complexity analysis, and problem complexity analysis. Design techniques analyzed will include divideandconquer, dynamic programming, greedy algorithms, backtracking, and branchandbound. These techniques can be considered general problem solving tools, whose applications are not limited to traditional computing and mathematical problems. Two factors make this point particularly important. First, more and more computing applications go beyond the traditional domain, and there are reasons to believe that this trend will strengthen in the future. Second, developing students’ problem solving skills has come to be recognized as a major goal of college education. Among all the courses in a computer science curriculum, a course on the design and analysis of algorithms is uniquely suitable for this task because it can offer a student specific strategies for solving problems. The course is organized around some fundamental strategies of algorithm design and algorithm design will be taught on a par with analysis. Some more abstract but very important topics will be also included: NPcompleteness, approximation algorithms, lowerbound limits.
Learning Outcomes
On
completion of this course, the student will have:
A basic understanding of
algorithm design and problem solving using fundamental techniques. 

The student will be able to
demonstrate the associations between problem solving, algorithm design, and
complexity analysis. 

Applied algorithm design, analysis,
and implementation in various applications. 

Developed creativity and
strategy skills for problem solving. 
Exams (2  40% each)
80%
Assignments (approx. 30; only some of them will be graded)
20%
95  100 A
90  94 A
87  89 B+
83  86 B
80  82 B
77  79 C+
73  76 C
70  72 C
67  69 D+
63  66 D
60  62 D
0  59 F
If you must miss an
exam, contact your instructor prior to the exam to schedule a time to
make it up. Late submission of assignments is generally not accepted. No
partial credit for late assignments will be offered.
The
slides for lectures can be found in the shared directory on Neve. Be ready to spend about two
hours to prepare for each class (Reading + Assignment). Exercises have hints at the end of the
textbook!
Dictionary of
Algorithms and Data Structures
Data
Structures and Algorithms many useful links by Prof. Toussaint
Date 
Topics 
Reading 
Assignments 
9/23 
General presentation 


9/24 
Introduction 
Ch. 1 
Ass. 1: Ex. 10 (a, b), pg. 8, due next class 
9/28 
Analysis framework  asymptotic notation 
2.1 2.2 
Ass. 2: Ex. 9, pg. 18, due next class 
9/29 
Analysis framework  asymptotic notation 
2.1 2.2 
Ass. 3: Ex. 3, pg. 59; Ex. 11, pg. 61, due next class 
9/30 
Mathematical analysis of nonrecursive
algorithms 
2.3 
Ass. 4: Ex. 6, pg. 68, due next class 
10/1 
Mathematical analysis of recursive algorithms:
backward substitution 
2.4, App. B 
Ass. 5: Ex. 9, pg. 78, due next class 
10/5 
Mathematical analysis of recursive algorithms: homogeneous
recurrences 
2.5, App. B 
Ass. 6: Ex. 8, pg. 78, due next class 
10/6 
Mathematical analysis of recursive algorithms: heterogeneous
recurrences, Master Theorem 
2.5, App. B 
Ass. 7: Ex. 1, pg. 76, due next class 
10/7 
Bruteforce algorithms 
3.13.2 

10/8 
Exhaustive search 
3.4 
Ass. 8: Ex. 3, pg. 106, due next class 
10/12 
DepthFirst and BreadthFirst Search 
3.5 
Ass. 9: Ex. 10, pg. 121 (a, b, c), due next class 
10/13 
Decreaseandconquer: Insertion sort 
4.1 
Ass. 10: Ex. 11, pg. 130, due next class 
10/14 
Topological sorting 
4.2 
Ass. 11: Ex. 9 & 10, pg. 137, due next class 
10/15 
Decreasebyaconstant factor algorithms: binary
search, fakecoin problem, multiplication a la russe,
Josephus problem 
4.4 
Ass. 12: Ex. 1, pg. 142, due next class 
10/19 
Variablesizedecrease algorithms: selection
problem, interpolation search 
4.5 
Ass. 13: Ex. 10, pg. 157, due next class 
10/20 
Variablesizedecrease algorithms: binary search
trees, the game of NIM 
4.5 
Ass. 14: Ex. 12, pg. 157, due next class 
10/21 
Exam I (open book, no electronic devices) 

10/22 
Divideandconquer: mergesort,
quicksort 
5.15.2 
Ass. 15: Ex. 13, pg. 167, due next class 
10/26 
Binary
tree traversal, multiplication of large integers, Strassen's matrix multiplication,
closestpair problem 
5.35.5 
Ass. 16: Ex. 11, pg. 175; Ex. 11, pg. 182, due next class 
10/27 
Transformandconquer: presorting, Gaussian elimination 
6.16.2 
Ass. 17 (on Neve), due next class 
10/28 
Balanced search trees, Heapsort, Horner's Rule, binary
exponentiation 
6.36.5 
Ass. 18: Ex. 7, pg. 206, due next class 
10/29 
Problem reduction 
6.6 
Ass. 19: Ex. 2, pg. 205, due next class 
11/2 
Spacetime tradeoffs: sorting by counting, string matching,
hashing 
7.17.3 
Ass. 20: Ex.
12, pg. 234, due next class 
11/3 
Dynamic programming, Knapsack Problem, Memory Functions 
8.18.2 
Ass. 21: Ex. 2, pg. 267, due next class 
11/4 
Optimal binary search trees, Warshall,
Floyd 
8.38.4 
Ass. 22:
Ex. 12, pg. 292, due next class 
11/5 
Greedy algorithms: Prim, Kruskal, 
9.19.2 
Ass. 23:
Ex. 1, pg. 311; Ex. 7, pg. 312, due next class 
11/9 
Dijkstra (singlesource shortest path) 
9.39.3 
Ass. 24 (on Neve) 
11/10 
Huffman trees 
9.4 
Ass. 25: Ex. 3, pg. 322; Ex. 10, pg. 343, due next
class 
11/11 
Veterans Day 


11/12 
Iterative improvement: Simplex 
10.1 

11/16 
Iterative improvement: maximum matching in
bipartite graphs,
the stable marriage problem 
10.310.4 
Ass. 24 due Ass. 26: Ex. 3, pg. 384, due next class Understand the Dental Matching Program Algorithm 
11/17 
Lowerbound arguments, decision trees 
11.111.2 
Ass. 27: Ex. 10 pg. 400, due next class, not
graded. Solution can be found here. 
11/18 
P, NP, and NPcomplete problems 
11.3 
Ass. 28: Ex. 5, pg. 410, due next class 
11/19 
P, NP, and NPcomplete problems 
11.3 
Ass. 29: Ex. 11, pg. 411, due next class 
11/23 
Backtracking: nqueens problem, subsetsum problem 
12.1 

11/24 
Branchandbound: assignment problem, knapsack problem,
traveling salesman problem 
12.2 
Ass. 30: Ex.5, pg. 440, due next class 
11/25 
Thanksgiving 

11/26 
Thanksgiving 

11/30 
Approximation algorithms for NPhard problems 
12.3 

12/1 
Algorithms for Solving Nonlinear Equations 
12.4 

12/2 
Solve a problem 


12/3 
Review 

12/8 
Final Exam (open book, no electronic devices) noon – 1:30 

Honor
Code: All work turned in for credit, including exams and all components
of the project, are to be the work of the student whose name is on the exam or
project. For all project components, the student can receive assistance from
individuals other than the instructor only to ascertain the cause
of errors. Thus you can get help if you need it to figure out why something
doesn't work. You just can't get help from anyone, other than the instructor or
TA, to figure out how to make something work. All solutions turned in for
credit are to be your individual work and should demonstrate your problem
solving skills, not someone else's. The following text
should appear on all assignments: “I pledge that I have neither given nor
received help from anyone other than the instructor for all program components
included here.”
You should write the
code for your programs yourself. Writing it yourself is the only way you will
learn. Do not work together to solve the
programming assignments to the extent that two programs are essentially the
same solution. All program solutions turned in for credit are to be your
individual work and should demonstrate your problem solving skills, not someone
else's. Since everyone is writing their own code, no two programs should be the
same or so similar that I could convert one to the other by a simple mechanical
transformation (e.g. changing variable names and comments). I consider this
plagiarism and a violation of academic code.
First violation:
Students must meet with the instructor. In most cases, the grade will be split
between the authors of the copied programs. Second violation: Students will
receive no credit for the assignment, an incident letter will be placed on file
in the Computer Science Department, and the matter referred to the Computer
Science Department Chair.
Class
Attendance: Class attendance is
expected and recorded.
ADA
Statement: Students with
disabilities who wish to set up academic adjustment in this class should give
me a copy of their "Confirmation of Eligibility for Academic
Adjustment" from the Disability Support Services Office as soon as
possible so we can discuss how the approved adjustment will be implemented in
this class. Students without this form should contact the Disability Support
Services Office, Buillon 205 or dssrecept@cwu.edu or 9632171.
Caveat: The schedule
and procedures for this course are subject to change. It is the student's
responsibility to learn of and adjust to changes.