Editing Optimization Theory (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Modeling 'The Diet Problem' (Linear Programming):'''
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def simple_optimize_profit(hours_avail, price_a, price_b, time_per_a, time_per_b):
    """
    Max Profit = (n_a * price_a) + (n_b * price_b)
    Constraint: (n_a * time_per_a) + (n_b * time_per_b) <= hours_avail
    """
    # If we only make A
    count_a = hours_avail // time_per_a
    profit_a = count_a * price_a
    
    # If we only make B
    count_b = hours_avail // time_per_b
    profit_b = count_b * price_b
    
    if profit_a > profit_b:
        return f"Best: Make {count_a} of A. Profit: ${profit_a}"
    else:
        return f"Best: Make {count_b} of B. Profit: ${profit_b}"

# 40 hours, Product A ($50, 2h), Product B ($30, 1h)
print(simple_optimize_profit(40, 50, 30, 2, 1))
# Even though A is 'more expensive', B is better because 
# you can make more of it in the same time.
</syntaxhighlight>

; Optimization in Action
: '''Logistics (The Traveling Salesman)''' → Finding the shortest route to visit 100 cities. It is a "Hard" problem that powers UPS and FedEx.
: '''Portfolio Optimization''' → Choosing a mix of stocks to get the highest return for the lowest risk.
: '''Backpropagation''' → The optimization algorithm that trains AI by minimizing the "Error" between the AI's guess and the truth.
: '''Structural Engineering''' → Designing a bridge with the minimum amount of steel that can still support 100 tons.
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