csce 3110 data structures and algorithm analysis
TRANSCRIPT
CSCE 3110Data Structures and Algorithm Analysis
Course Webpage and Schedule
Course webpage can be found at: http://www.cse.unt.edu/~huangyan/3110
The lecture schedule is:
Tuesdays & Thursdays 2:30PM- 3:50PM @ NTRP B142
Office hour: Class day @ 1:15PM-2:15PM and 4:00-5:00pm or by appointment
Objectives
Understand dictionary/search data structures (lists, trees, hash tables). Understand graph representations and algorithms. Understand time and space analysis for both iterative and recursive algorithms and be able to prove the correctness a non-trivial algorithm. Be able to translate high-level, abstract data structure descriptions into concrete code. Understand how real-world problems map to abstract graph problems. Be able to communicate clearly and precisely about the correctness and analysis of basic algorithms (both oral and written communication).
Data structures will include lists, vectors, queues, stacks, trees, hash-tables, and graphs. Algorithms include searching, sorting using the above data structures, iterative, and recursive algorithmsOn assignments, students are asked to implement data structures and algorithms using a high-level programming language. On exams, students are asked to solve problems using analytical methods and programming skills acquired from lectures and assignments to demonstrate knowledge and comprehension of course material.
Prerequisites CSCE 2050 and MATH 2770
Text Book “Data Structures & Algorithm Analysis in C+
+” (3rd Edition). Mark Allen Weiss
Other Reference “C++ How to Program”, 5th edition, H. M.
Deitel, P. J. Deitel
Assumed Background
Assumed Background: It is assumed that the student has taken CSCE 2050 and MATH 2770, and thus is knowledgeable about the following: From CSCE 2050:
Experience with non-trivial software design; object-oriented design and abstract data types; C++ language features including pointers, classes, and templates; linear and binary searching; recursion; basic data structures (linked lists, stacks, and queues); basic sorting algorithms (insertion, selection, and merge sort); simple time analysis of algorithms.
From Math 2770: Proof techniques (induction, contradiction); mathematical structures (sets, relations, trees, graphs); important mathematical manipulations (sums, combinatorics).
What else do you need?
Unix account from the department machinesTurn in homework using project commandSubscribe to the class email list (link available from the class webpage) to be updated promptly about class information
Grading Information
Assignments 40%Projects 20%Two exams: 35%Class participation: 5%
Grading scale (based on 100 points) A 90-100 pointsB 80-89 points C 70-79 points D 60-69 points F below points
Programming Assignments
Each assignment may have a mixture of written and programming components.Each assignment will specify the material to be turned in.All programming will be in C/C++, and must compile on a University Unix/Linux machine. No credit will be given for programs that do not compile.
Project
You have the option to work alone or form a group with another student for the term projectIf you choose to work in a team, both students will receive the same marks for the projectThe description of the term project will be posted soon (in two weeks) and you are encouraged to work on it as soon as possibleYou will have to make a 10 minutes presentation at the end of the semester
Important Notes
All assignments are due before class of the due date. A late penalty of 10% will be applied to all late assignments for up to 3 calendar days. Assignments that are not turned in 3 days after the due date will not be accepted.
All holidays and weekends will be counted as calendar days
Missed exams will be granted zero points. Illness and severe circumstances may be accommodated only with solid evidence. Email me before exam and provide solid evidence whenever possible.
Important Notes
Academic misconduct includes the submission of program code or assignment answers that appear so similar to another person’s work as to be semantically indistinguishable. Misconduct cases will be handles swiftly, discreetly,and summarily in accordance with University principles.
Questions?
What this course is about ?
Data structures: conceptual and concrete ways to organize data for efficient storage and efficient manipulationEmployment of this data structures in the design of efficient algorithmsAnalysis of the complexity of your algorithms
Why do we need them
Requirements for a good software:Clean DesignEasy maintenanceReliable (no core dumps)Easy to useFast algorithms
Efficient data structuresEfficient algorithms
Why do we need them ?
Computers take on more and more complex tasks
Imagine: index of 8 billion pages ! (Google)
Software implementation and maintenance is difficult.
Clean conceptual framework allows for more efficient and more correct code
How Fast Are Our Computers?
In current computer architectures, we can perform a linear scan of
1 megabyte of data in 1 second1 gigabyte of data in 2 minutes1 terabyte of data in 2 days
How long does it take took to scan 8 billion pages (google)?
Now you are searching the word “insomnia”!
Google index 8 billion pages.Assume each page has 100 words (which is a humble number)Each word has 10 letters, each letter is one byte
It takes around 21 hours to perform a liner scan of 8 billion pages!Word Wide Wait!
Topics
Algorithm AnalysisArrays, Stacks and QueuesVectors, lists and sequencesTreesHeaps / Priority QueuesBinary Search Trees – Search TreesHashing / DictionariesSortingGraphs and graph algorithms
Example
What to do?Sol. 1 Sequential matchingSol. 2 Binary searching:
- order the words- search only half at a time
Ex. Search 25 in 5 8 12 15 15 17 23 25 27 25 ? 15 15 17 23 25 27
25 ? 23 23 25 27 25 ? 25
How many steps?
One Step Further
Given n numbers, how many steps do we need to search a number from the n numbers, using
Linear searchBinary search (when and when not to use binary search)?
Some example data structures
Set Stack Tree
Data structure = representation and operations associated with a data type
What will you learn?
What are some of the common data structures
What are some ways to implement them
How to analyze the complexity of your algorithms using the data structures
How to use them to solve practical problems