Analysis of Algorithms and Heuristic Problem Solving
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Weekly outline
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Syllabus # Topic Description 1 Introduction Course contents, review of what you already know about computational complexity 2 Computational Complexity Definitions of Θ,O,Ω,o,ω , analysis of recursive algorithms, substitution method, recursive tree method,
Recipes for divide and conquer algorithms: the master theorem, the Akra-Bazzi theorem
http://videolectures.net/mit6046jf05_introduction_algorithms/3 Linear recurrences Solving linear recurrences with annihilators 4 Probabilistic analysis Indicator random variables, probabilistic analysis of algorithms 5 Randomized algorithms Randomized algorithms, pseudo-random numbers,
http://videolectures.net/mit6046jf05_leiserson_lec04/6 Amortized analysis Amortized analysis of computational complexity,
http://videolectures.net/mit6046jf05_leiserson_lec13/7 Analysis of multithreaded algorithms Read Chapter 27 in the textbook 8 Approximation algorithms Read Chapter 35 in the textbook, revise P and NP algorithms by reading Chapter 34 9 Linear programming for problem solving Read Chapters 29 and 35.4 in the textbook
The code of LP example in R Datoteka10 Optimization and local search Read Chapter 12 in J. Kleinberg, E. Tardos: Algorithm Design. Pearson, 2006 11 Metaheuristics Simulated annealing, Nash equilibrium and social choice, tabu search, variable neighborhood search, guided local search
Read the corresponding chapters in M. Gendreau, J.-Y. Potvin: Handbook of Metaheuristics, Springer, 2010
TABU search, VNS, GLS12 Population methods Memetic algorithms, swarm intelligence 13 Differential evolution Optimization with differential evolution
Description of DE in textbook Computational Intelligence - An Introduction
Das et al. (2016): Recent advances in differential evolution – An updated survey, Datoteka Datoteka14 Biologically inspired metaheuristics many optimization methods taking inspiration in the nature and society
A survey paper on biologically inspired metaheuristics Datoteka
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