Optimisation in Python and its application in carbon portfolio
In this 3-hour tutorial, you will learn the basics of mathematical optimization and how to solve an optimization problem in Python with tools that are easily available. We will then apply our learning to construct a carbon portfolio using optimization.
Dr Jessica Leung is a lecturer in Business Analytics at the Econometrics and Business Statistics Department at Monash University. Her research focuses on convex and combinatorial optimization in business applications. Jessica teaches undergraduate and postgraduate courses in business statistics, management science, optimization, applied linear algebra, and visual data analytics.
- Session 1: Introduction to optimization
- What is a mathematical optimization problem?
- Examples: linear programming, mixed integer programming, quadratic programming and quadratically constrained programming
- Session 2: Using the python optimization tools
- Introducing scipy.optimize and cvxpy
- Solving optimization problems using scipy.optimize
- Session 3: Carbon portfolio diversification application
- Introducing Markowitz portfolio optimization
- Introducing carbon metrics in the mathematical optimization model
Participants are expected to have Anaconda and Python installed and are familiar with Jupyter Notebook or other Python editor.