STA5071Z - Simulation And Optimisation

15 credits at NQF level 9

Entry Requirements:

Acceptance into Master’s programs in Advanced Analytics, Data Science or Biostatistics, and/ or statistical background as deemed sufficient by the Head of Department.

Course Outline:

This module is split into three sections: Simulation (Random Number Generation, Monte Carlo Methods, Statistical Analysis of Simulated Data, Variance Reduction, Bootstrap Methods, Markov Chain Monte Carlo), Fundamentals of Linear and Nonlinear Optimization (Unconstrained and Constrained Optimization, Kuhn-Tucker Duality, Convexity, Quadratic Programming, Dynamic Programming, Stochastic Programming) and Stochastic Methods in Optimization ("No Free Lunch" Theorems, Metaheuristics, Random Search, Simulated Annealing, Evolutionary and Genetic Algorithms, Partition Algorithms). The course may not be offered every year.