ENGR 103 — Week 05: Simple Algorithms: Approximation

📖 Weekly Overview

Approximation algorithms let us solve problems that have no exact analytical solution — like finding optimal substrate concentrations in Monod kinetics, or locating equilibrium points in complex biological systems.

🎯 Learning Objectives

By the end of this week, you will be able to:

Implement exhaustive enumeration for numerical approximation
Choose appropriate epsilon values and step sizes
Apply approximation to enzyme kinetics and growth problems
Understand absolute vs. relative error

📚 Required Reading

Guttag, "Introduction to Computation and Programming Using Python" (3rd Ed)
Week 05 Reading: Ch 3.2–3.3
Complete before the first lecture of the week.

📅 This Week's Schedule

Monday: Monday: Exhaustive approximation
Friday: Friday: Float representation and bisection preview.

🔬 Studio 2 (this week): Numerical methods for environmental analysis

✅ Assignments Due This Week

Week 05 Quiz — Due Sunday 11:59 PM on Canvas (15 questions, 15 pts)
Week 05 Assignment — Due Sunday 11:59 PM on Canvas (3 coding problems)
Studio 2 (this week): Numerical methods for environmental analysis — During studio session

🔗 Resources

Lecture slides available on Canvas before each class
Textbook: Guttag Ch 1 available free at MIT OpenCourseWare
Python documentation: docs.python.org
Office hours: Check Canvas calendar for times and Zoom/room info
Course Piazza: Post questions (anonymously if preferred)