This was sort of a strange concept but page 381 broke down how each step was obtained was amazing! I believe the constraint on a function is actually a second function that limits the first. The hardest part was the Lagrange multiplier.It was interesting that it is a rate of the change in the optimum value of f over the change in g. but i don't understand the purpose of the Lagrange multiplier, what does it have to do with constrained optimization?
On a random note... are constraints represented graphically as contour maps?
What was neat was how useful constrained optimization is, it really applies to real world situations. Like businesses and budgets. Seriously if you do not put constraints on functions than you can see all possible values, but not the ones that are important to a situation.
Seriously we are in college we need to budget just about everything from time to money to life!
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