| Code |
16671
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
|
| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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|
Entry requirements |
Python knowledge.
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Learning outcomes |
The objectives of this UC focus on providing students with algorithmic complexity analysis techniques, different complex data structures, and a set of algorithms for solving computational problems.
At the end of this curricular unit, the student must be able to:
- Explain the usefulness and complexity of different data structures, as well as being able to combine different data structures to solve problems.
- Analyze the computational complexity (temporal and spatial) of a given algorithm.
- Implement different algorithms and data structures to solve complex problems.
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Syllabus |
1 - Recursion
2 - Analysis of temporal and spatial complexity
3- Sorting algorithms
4 - Data structures:
- Vectors
- Dynamic vectors
- Lists, doubly linked lists and variants
- Queues, stacks, and their variants (priority, double-entry)
- Trees
- Computational representation of graphs
5 - Search algorithms
6 - Problems with trees and graphs
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Main Bibliography |
Theoretical classes PDFs.
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Introduction to Algorithms (Fourth Edition), The MIT Press, 2022.
Brad Miller and David Ranum. Problem Solving with Algorithms and Data Structures using Python, Luther College, 2006.
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Language |
Portuguese. Tutorial support is available in English.
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