DD2458 Problem Solving and Programming under Pressure 9.0 credits
Problemlösning och programmering under press
Successful problem solving in computer science requires a solid theoretical foundation as well as ability to apply the theory to practical problem solving. The aim of this course is to develop your ability to apply knowledge of algorithms, data structures, and complexity theory to given problems. As a professional it is useful to be able to analyze a problem, judge the efficiency of proposed algorithms, and to implement them quickly and correctly. In this course, you will practice this by solving a number of homework assignments and while working under time constraints during problem solving sessions.
Educational levelSecond cycle
Academic level (A-D)C
Subject areaComputer Science and Engineering
Grade scaleA, B, C, D, E, FX, F
Autumn 17 P1 (4.5 credits), P2 (4.5 credits)
2017 week: 35
2018 week: 3
Language of instruction
Number of lectures
Number of exercises
Form of study
Number of places *
*) If there are more applicants than number of places selection will be made.
P1: C1, H1, C2, H2. P2: C1, H1, C2, H2. more info
Searchable for students from year 3 and for students admitted to a master programme.
Part of programme
- Master's Programme, Computer Science, 120 credits, year 1, CSST, Recommended
- Master's Programme, Computer Science, 120 credits, year 1, CSTC, Recommended
- Master's Programme, Computer Science, 120 credits, year 2, CSST, Recommended
- Master's Programme, Computer Science, 120 credits, year 2, CSTC, Recommended
Intended learning outcomes
The overarching goal of the course is that the students should be able to use programming as a tool for problem solving, and should be able to apply theoretical knowledge from other computer science courses to solve practical problems. The course has a large focus on going all the way from theory (in the form of algorithm design) to practice (in the form of a working program).
After passing the course the student should be able to:
- use algorithm design methods such as greedy algorithms, dynamic programming, decomposition, and combinatorial search to construct algorithms for solving given problems,
- apply basic algorithms in areas such as graph theory, number theory, and geometry on given problems and adapt them to problem-specific circumstances,
- analyze the efficiency of different algorithms in order to decide which ones are sufficiently efficient in a given context,
- compare different problems with respect to difficulty,
- implement algorithms and data structures given abstract specifications,
- identify bugs in others' solution attempts on a problem,
- communicate with others during problem solving in groups,
- present algorithms, data structures, and problems verbally in a concise and lucid way.
The goals are attained by solving number of homework assignments, implementing a small library of algorithms and data structures, solving problems in groups during problem solving sessions, and by presenting solutions to homework assignments.
Course main content
Algorithms: computational geometry, graph algorithms, number theoretic algorithms, string matching. Design and analysis of algorithms: dynamic programming, amortized analysis, estimating the complexity of an algorithm. Programming skills mainly in C and Java.
One of the courses DD1352 Algorithms, Data Structures, and Complexity, DD2352 Algorithms and Complexity, or equivalent.
Course literature will be announced on the course web page at least 4 weeks before course start.
- LAB1 - Programming Contests, 4.5, grade scale: A, B, C, D, E, FX, F
- ÖVN1 - Exercises, 4.5, grade scale: A, B, C, D, E, FX, F
The grade is based on the number of solved problems of the various kinds, and, to some extent, on the quality of the presentations. As the problems are of greatly varying difficulty, the student that solves many problems will automatically also solve a number of harder problems, thereby motivating the higher grade. For grade A one additionally needs to solve a given number of extra difficult problems.
In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.
Requirements for final grade
Examination can only be done during the course.
CSC/Theoretical Computer Science
Per Austrin, e-post: email@example.com
Per Austrin <firstname.lastname@example.org>
The number of participants is limited to 25.
The course is only given if we have sufficient teaching resources.
Please discuss with the instructor.
Course syllabus valid from: Autumn 2016.
Examination information valid from: Autumn 2007.