Lecture topics:
Analysis of recursive algorithms: substitution method, solution for divide and conquer approach, Akra-Bazzi method.
Probabilistic analysis: definition, analysis of stochastic algorithms.
Randomization of algorithms.
Amortized analysis of algorithm complexity.
Solving linear recurrences.
Approximation algorithms.
Analysis of multithreaded and parallel algorithms.
Approximation algorithms.
Combinatorial optimization, local search, simulated annealing.
Linear programming for problem solving.
Metaheuristics and stochastic search: guided local search, variable neighbourhood search, and tabu search.
Population methods: genetic algorithms, particle swarm optimization, differential evolution
Machine learning in combinatorial optimization
