Algorithm Engineering

Course Code: 
Spring Semester
Credit Points: 

Course outline

  • Motivation, peculiarities and importance of algorithm engineering. The algorithm engineering life cycle.
  • Development of software platforms and libraries that ease the efficient implementation and experimental evaluation of algorithms and data structures.
  • Methodological issues regarding the empirical research of algorithms and data structures. Application of the scientific experimental method. Reproduction of experiments. Creating and searching for proper data sets.
  • Program correctness checking. Certifying algorithms and programs.
  • Efficient implementation and experimental evaluation of classic and advanced algorithms and data structures. Case studies: graph algorithms, shortest path algorithms, maximum flow algorithms.
  • Heuristic methods for enhancing implementations and boosting their performance.
  • Arithmetic precision errors and methods for their handling.
  • Methodological issues in the process of converting user requirements to efficient and useful algorithmic software.
  • Implementation environment: C++ along with the algorithmic software platforms/libraries LEDA, STL and BOOST.
  • Special topics: algorithm animation, generative programming, template metaprogramming, policy-based class design. 

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