LOGISTIC MANAGEMENT | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP3005 | Analysis of Algorithms | Spring | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
Course Lecturer(s): |
Dr. Öğr. Üyesi TEVFİK AYTEKİN Prof. Dr. NAFİZ ARICA Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
Recommended Optional Program Components: | None |
Course Objectives: | The objective of the course is to introduce the fundamental mathematical tools needed to analyze algorithms, basic algorithm design techniques, advanced data structures, and important algorithms from different problem domains. |
The students who have succeeded in this course; I. Become familiar with some major advanced data structures and algorithms. II. Become familiar with mathematical tools used in analyzing algorithms. III. Be able to analyze the asymptotic running time of an (iterative/recursive) algorithm. IV. Be able to make best/worst/average case analysis of algorithms. V. Become familiar with important algorithm design paradigms. VI. Be able to decide which data structure/algorithm among a set of possible choices is best for a particular application. VII. Be able to recognize and distinguish efficient and inefficient algorithms. VIII. Be able to design efficient algorithms for new problems using the techniques learned and apply/report these solutions in an intra-discipline project group. |
Introduction, asymptotic notation, empirical analysis of algorithms, designing algorithms, amortized analysis, brute force algorithms, divide and conquer algorithms, transform and conquer algorithms, space and time trade-offs, dynamic programming, greedy algorithms, advanced data structures, B-trees, Insertion and Deletion from B-trees, graphs and graph algorithms, P, NP, and NP-complete problems. |
Week | Subject | Related Preparation |
1) | Introduction, asymptotic notation. | |
2) | Empirical analysis of algorithms, analysis of algorithms, amortized analysis | |
3) | Recurrences, substitution method, recursion-tree method, master method. | |
4) | Brute Force Algorithms | |
5) | Divide and Conquer Algorithms | |
6) | Merge sort, quicksort, randomized quicksort, binary search | |
7) | Transform and Conquer Algorithms: Solving systems of linear equations with Gaussian ination elimination, Balanced Search Trees, Heaps and Heapsort, Horner's Rule and Binary Exponentiation | |
8) | Space and Time Trade-offs: Input Enhancement (Counting based sorting, string matching), Prestructuring (Hashing, Hash functions, open addressing). | |
9) | Midterm | |
10) | Dynamic Programming: Coin-row problem, Knapsack problem, Longest common subsequence. | |
11) | Dynamic Programming: Knapsack problem, Longest common subsequence. | |
12) | Greedy Algorithms: Activity selection, Huffman codes, Prim’s algorithm, Kruskal’s Algorithm | |
13) | Single-source shortest paths: The Bellman-Ford algorithm, Dijkstra's algorithm. | |
14) | P, NP, and NP-complete problems |
Course Notes / Textbooks: | Anany Levitin, The Design and Analysis of Algorithms, Pearson International Third Edition. Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C., Introduction to Algorithms (3rd Edition), MIT Press, 2009. Sanjoy Dasgupta , Christos Papadimitriou, Umesh Vazirani, Algorithms, McGraw-Hill Education. |
References: | Yok - None |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 2 | % 10 |
Project | 1 | % 20 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 42 |
Project | 7 | 21 |
Quizzes | 6 | 12 |
Midterms | 5 | 28 |
Final | 5 | 35 |
Total Workload | 138 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | To correctly identify the problems and to be able to ask the correct questions | |
2) | To have the ability for problem solving and to utilize analytical approach in dealing with the problems | |
3) | To be able to identify business processes and use them to increase the productivity in logistics system. | |
4) | To be fully prepared for a graduate study | 2 |
5) | Awareness of the new advancements in Information and Communications Technologies (ICT) and to be able to use them in logistics management effectively. internet and the electronic world | |
6) | To understand the components of logistics as well as the importance of the coordination among these components. | |
7) | To know the necessary ingredients for improving the productivity in business life | |
8) | To think innovatively and creatively in complex situations | 4 |
9) | To act and think both regionally and internationally | |
10) | To understand the demands and particular questions of globalization | |
11) | Aware of the two way interaction between globalization and logistics; as well as to use this interaction for increasing the productivity. | |
12) | To be able to use at least one foreign language both for communication and academic purposes | 2 |
13) | To acquire leadership qualities but also to know how to be a team member | |
14) | To understand the importance of business ethics and to apply business ethics as a principal guide in both business and academic environment |