Simulated annealing (dual_annealing) in SciPy - Mini Project: Build & Apply

Optimize a Simple Function Using Simulated Annealing (dual_annealing)

📖 Scenario: You are working as a data scientist and want to find the minimum value of a mathematical function. This is like trying to find the lowest point in a hilly landscape by exploring smartly.

🎯 Goal: Use the dual_annealing method from scipy.optimize to find the minimum of a given function within a specific range.

📋 What You'll Learn

Create a function to optimize

Set the bounds for the function inputs

Use dual_annealing to find the minimum

Print the minimum value found

💡 Why This Matters

🌍 Real World

Simulated annealing helps find good solutions in complex problems like scheduling, routing, or tuning machine learning models.

💼 Career

Understanding optimization techniques like dual_annealing is useful for data scientists and analysts who need to improve models or find best parameters.

1

Define the function to optimize

Write a function called func that takes one input x and returns the value of (x[0] - 3) ** 2 + (x[0] + 1) ** 2. This function represents the landscape you want to find the lowest point of.

SciPy

# Define the function func that takes x and returns the sum of squares expression
# Your code here

Need a hint?

Remember, x will be a list or array, so access the first element with x[0].

2

Set the bounds for the input variable

Create a variable called bounds and set it to a list with one tuple: (-5, 5). This limits the search for the minimum to values between -5 and 5.

SciPy

def func(x):
    return (x[0] - 3) ** 2 + (x[0] + 1) ** 2

# Set bounds for x between -5 and 5
# Your code here

Need a hint?

Bounds must be a list of tuples, even if there is only one variable.

3

Use dual_annealing to find the minimum

Import dual_annealing from scipy.optimize. Then create a variable called result by calling dual_annealing with func and bounds as arguments.

SciPy

def func(x):
    return (x[0] - 3) ** 2 + (x[0] + 1) ** 2

bounds = [(-5, 5)]

# Import dual_annealing and run optimization
# Your code here

Need a hint?

Use from scipy.optimize import dual_annealing to import the function.

4

Print the minimum value found

Write a print statement to display the minimum value found by dual_annealing. Use result.fun to get the minimum function value.

SciPy

from scipy.optimize import dual_annealing

def func(x):
    return (x[0] - 3) ** 2 + (x[0] + 1) ** 2

bounds = [(-5, 5)]

result = dual_annealing(func, bounds)

# Print the minimum function value found
# Your code here

Need a hint?

The minimum value should be 8.0 because the function has its lowest point at x = 1.