Overview_06.ipynb

Kernel: Python 3 (system-wide)

# execute to import notebook styling for tables and width etc.
from IPython.core.display import HTML
import urllib.request
response = urllib.request.urlopen('https://raw.githubusercontent.com/DataScienceUWL/DS775v2/master/ds755.css')
HTML(response.read().decode("utf-8"));

Overview 06: Global Optimization 1

Topics

• Simulated Annealing and the simanneal package

• Bayesian Optimization

Learning Outcomes

The student will be able to:

• use simulated annealing to identify good solutions to complex optimization problems

• use Bayesian optimization to optimize functions with noise or that are very expensive to evaluate

• recognize that stochastic optimization can identify good solutions that are not necessarily the best solutions

Student "To Do" Checklist

• Reading

  • 14.1, 14.3, 14.5 in Hillier and Liebermann.

  • There are some additional readings (from websites) linked in the Bayesian Optimization part of the .esson notebook.

• Work your way through the Jupyter notebook called Lesson_06.ipynb. This is the main presentation and replaces the Storybook presentations used in our other courses. Use the self-assessments to measure your understanding.

• Complete the problems in Homework_06.ipynb in CoCalc prior to the due date.

Use Piazza to ask questions when you have them and be sure to check Piazza regularly so you don't miss out on any good Q & A or other discussions.