Course detail

Algorithms and Data Structures

FEKT-BPC-ALDAcad. year: 2020/2021

The first part of the course is focused on introducing students to the basic concepts: algorithm, time and memory complexity of the algorithm, data container / collection.
The second part of the course deals with the concepts: abstract data type: (Vector, Stack, Queue, Set, Tree) and the use of iterators for these data types.
In the third part, students will learn about recursive and non-recursive algorithms, sorting and searching algorithms.

Language of instruction

Czech

Number of ECTS credits

7

Mode of study

Not applicable.

Learning outcomes of the course unit

The student is able:
- analyze the task using a flowchart;
- to determine its time and memory complexity for a simple algorithm;
- use basic abstract data types (Vector, Stack, Queue, Set, Tree) and use iterators for these data types.
- solve the problem using recursive and non-recursive algorithms;
- design and implement basic sorting and searching algorithms;
- correctly determine the data type for a given type of calculation (basic data types, pointer type to data type, and composite data type);
- work with dynamically allocated memory (acquisition and release of memory, manipulation of data in allocated memory);
- work with standard inputs, outputs and files;

Prerequisites

Attending BPC-UDP course or similar.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Techning methods include lectures and computer laboratory lectures. Students have to create assignments during the computer laboratory lectures.

Assesment methods and criteria linked to learning outcomes

Up to 40 points for the laboratory lectures (2 tests, up to 20 points each). Minimal needed points from laboratory lectures is 15.
Up to 60 points for the final examination. Minimal needed points from final examination is 20.
The course exam will take place in person.

Course curriculum

1. Introduction to the course, the concept of algorithm, time and memory complexity, dynamic memory allocation, API design for working with files.
2. Abstract data types, signature of abstract data type, concept (collection / container), concept of linear and nonlinear data structure (array, one-way bound list), description and properties of ADT: Stack, implementation of ADT Stack using array and single linked list. ADT TStack_array, ADT TStack_list.
3. Description and properties of ADT: Queue, Set, ADT TQueue_list, ADT TQueue_array, concept of iterator, use of iterators in ADT.
4. Description and properties of ADT: Set, Tree, (SkipList), unordered and ordered collections.
5. Introduction to sorting algorithms, properties of sorting methods, external and internal sorting, "in situ / in place" sorting. Algorithms Insert Sort, derivation of algorithm complexity.
6. Sorting by direct exchange (BubbleSort), modification of BubbleSort algorithm, derivation of algorithm complexity, sorting by direct selection (SelectSort), derivation of algorithm complexity
7. Recursive and iterative algorithm, use of recursion to calculate Fibonacci sequence, Wildcard matching (wildcards? *). Variants of recursive algorithms: direct, indirect recursion (recursion with auxiliary function).
8. More efficient methods of internal sorting - Shell Sort, Quick Sort
9. Trees - basic information, sorting using a heap
10. External sorting with the same number of input and output files, deriving the complexity of the algorithm. External sorting using internal sorting, complexity derivation.
11. Search in linear data structure, Binary field search, algorithm complexity. Binary search trees, determining the complexity of the algorithm.
12. Hashing.
13. AVL trees, B-trees, end of the course.

Work placements

Not applicable.

Aims

The aim of the course is to familiar students with basic concepts of algorithm and data structures such as: the concept of algorithm, computational and memory complexity of the algorithm, abstract data type, recursive and non-recursive algorithms, sorting and searching algorithms.

Specification of controlled education, way of implementation and compensation for absences

The computer exercises is mandatory, the properly excused missed computer exercises can be compensate.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Basic literature

VEČERKA, Arnošt. Základní algoritmy. Skriptum Olomouc 2007. 91 s. (CS)
WRÓBLEWSKI,Piotr. Algoritmy – Datové struktury a programovací techniky. Brno: Computer Press, 2004. 351 s. ISBN 80-251-0343-9 (CS)
PROKOP, Jiří. Algoritmy v jazyku C a C++. Praha: Grada Publishing, 2008. 176 s. ISBN 978-80-247-3929-8 (CS)

Recommended reading

Not applicable.

eLearning

Classification of course in study plans

  • Programme BPC-AMT Bachelor's, 1. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1 Course overwiev. Basic program parts and the program design. Compilation. Optimalisation. Preprocessor commands.
2 Algorithms - task analysis, block diagrams, how to select variable type, sort algorithms.
3 Standard input and output. Standard header ctype.h
4 Bit operations. File manipulation.
5 Enumeration type and examples of its usage. Standard header math.h.
6 C array data type. Using pointers for value manipulation.
7 Pointers and functions. Arrays and pointer, pointer arithmetics.
8 Memory alocation - stdlib.h. More dimensional arrays. Array of pointers. Pointer to function.
9 Life cycle and usability of diffrend value types. C strings, manipulation with C strings. Standard header - string.h
10 Data structures. Manipulation with data structures and their members.
11 Inline functions. Linked list. Binary tree.
12 Literals. Complex and _Bool data types
13 Type qualifiers - const, volatile, restrict. Programming styles.


Exercise in computer lab

39 hours, compulsory

Teacher / Lecturer

Syllabus

1 Simple algorithms - task analysis, inputs, outputs, functions
2 Recursive functions and algorithms.
3 Bit operations - bit fields, bit manipulation, bit masking.
4 Base64 algorithm - coding and decoding data.
5 Test 1.
6 Sort algorithms, comparing.
7 State machine implementation.
8 Array data type. Pointer manipulation.
9 Multidimensional data manipulation.
10 Test 2.
11 Data structures. List implementation.
12 Linked list operations.
13 Binary trees.

eLearning