A first graduate course in algorithms, this course assumes minimal background, but moves rapidly. The objective is to learn the main techniques of algorithm analysis and design, while building a repertory of basic algorithmic solutions to problems in many ...
This course is an introduction to linear and discrete optimization.Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to prove theorems. ...
Mainly based on the discussion of peer reviewed academic papers, the course introduces non economists to the main types of applied models used in environmental economic analysis: linear programming, partial and general equilibrium, game theory, and agent b ...
This course introduces the theory and applications of optimization. We develop tools and concepts of optimization and decision analysis that enable managers in manufacturing, service operations, marketing, transportation and finance to transform data into ...
This course describes theory and methods for Reinforcement Learning (RL), which revolves around decision making under uncertainty. The course covers classic algorithms in RL as well as recent algorithms under the lens of contemporary optimization. ...
Methods for the rational use and conversion of energy in industrial processes : how to analyse the energy usage, calculate the heat recovery by pinch analysis, define heat exchanger network, integrate heat pumps and cogeneration units and realise exergy an ...
The course aims to introduce the basic concepts and results of integer optimization with special emphasis on algorithmic problems on lattices that have proved to be important in theoretical computer science and cryptography during the past 30 years. ...