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Mathematical Optimization
Course - MasterMathematical optimization is among the most important instruments of
Prescriptive Analytics and is used to take optimal decisions based on
quantitative arguments. Our daily life is full of examples of the
importance of optimization: for most trucks on the road, origin,
destination, load and even its route have been determined by an
optimization algorithm, leading to increased efficiency and lower
environmental impact. The battery life of your phone would be
significantly shorter if the chip lay-out was not optimized.
Side-effects of radiotherapy would be more severe if cancer treatment
was not personalized with state-of-the-art optimization algorithms.This course will make you familiar with translating practical problems
into optimization models, and with solving those models. Despite the
goal being modeling and solving such problems, this course teaches the
fundamental results from the mathematics of optimization, including
optimality conditions, duality, stochastic optimization, and robust
optimization.The course covers linear optimization as well as its generalizations
(conic and convex optimization). We will also consider optimization
under uncertainty. Optimization models will be implemented and solved
using mostly software that is freely available to all (e.g. Python) and
occasionally software that is free for academics but requires purchasing
for commercial use.