# Advanced Programming Laboratory

**Year**

3

**Academic year**

2017-2018

**Code**

01000301

**Subject Area**

Computer Science

**Language of Instruction**

Portuguese

**Mode of Delivery**

Face-to-face

**Duration**

SEMESTRIAL

**ECTS Credits**

6.0

**Type**

Compulsory

**Level**

1st Cycle Studies

## Recommended Prerequisites

Basic knowledge of algorithms, procedural and object orientered programming, data structures and mathematics (course units Introduction to Programming and Problem Solving, Algorithms and Data Structures, Principles of Procedural Programming, Object Oriented Programming and Discrete Structures).

## Teaching Methods

The goal of theory classes is to deliver theoretical concepts by the teacher, to present and discuss case-studies, to present the programming problems and to solve less complex problems, in a team. The aim of lab classes is to help students to solve the programming problems, to accompany the students in their project and to perform defenses.

## Learning Outcomes

To strengthen the ability to solve programming problems by algorithmic paradigms in different problem domains. Starting from a verbal problem descrition, the student must be able, himself or within a group:

• To understand the problem and relate it to other known problems;

• To identify algorithmic paradigms that are suitable for the problem at hand;

• To design particular algorithms for solving the problem at hand, based on the paradigms that are taught in class;

• To implement algorithms in a modular way, using appropriate data structures;

• To understand the complexity bounds of the algorithms;

• To explain and justify the options that were taken during the problem solving process.

Main skills to acquire are:

1 )Analysis and synthesis, problem solving

2) Critical reasoning

3) Autonomous learning, application of theory into practice.

Secondary skills are:

1) Decision making

2) Teamwork

3) Creativity and adaptability to new scenarios.

## Work Placement(s)

No## Syllabus

- Introduction
- Problem modelling and problem solving
- Computational complexity
- Algorithm paradigms
- Recursive search
- Backtracking
- Dynamic programming
- Greedy algorithms
- Branch-and-bound
- Applications
- Graphs
- Network flow
- Computational geometry

(optional) Other problems such as: numerical problems, matching problems, scanning and parsing problems, bioinformatics problems, cryptography, etc.

## Head Lecturer(s)

Luís Filipe dos Santos Coelho Paquete

## Assessment Methods

Assessment

*1) Solving, individually, the programming problems 2) Practical assessments; 3) Project; 4) Individual defenses of the grades obtained.: 100.0%*

## Bibliography

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 3rd ed., 2009.

S.Skiena and M.Revilla, Progamming Challenges, 2003.

D. Knuth, The Art of Computer Programming.

S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani, Algorithms (draft)

J. Kleinberg, and E. Tardos, Algorithm Design, 2005

R. Sedgewick and K. Wayne, Algorithms, 4rd ed, 2011

B. Vöcking, H. Alt, M. Dietzfelbinger, R. Reischuk, C. Scheideler, H. Vollmer, D. Wagner (eds), Algorithms Unplugged, 2011