EGR 103/Fall 2022/Lab 10

Assignment

You will be using nonlinear regression as well as function minimization to solve several engineering problems. The main Python methods you will be using are:

• scipy.optimize.curve_fit
• scipy.optimize.fminbound
• scipy.optimize.fmin

Note that generally we will bring in scipy.optimize with

import scipy.optimize as opt

so the function calls will look like

opt.curve_fit()
opt.fminbound()
opt.fmin()

In the documentation on Scipy, they bring in the entire optimize package with

from scipy import optimize

so their function calls look like

optimize.curve_fit()
optimize.fminbound()
optimize.fmin()

Chapra 15.11

• See Python:Fitting#Nonlinear_Regression
• For the initial guesses, make sure you understand the subscripts for the parameters and then figure out how to approximate their values from the information provided in the problem.

Chapra 15.22

• See Python:Fitting#Nonlinear_Regression
• For the initial guesses, mainly note that choosing $$b=0$$ would be a terrible idea.

Chapra 7.16

• Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.

Chapra 7.25, 7.26, 7.27

• For the initial guesses, you can use [0, 0]

Chapra 7.31

• Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.

Chapra 7.34

• Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.

Chapra 7.36

• Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.