Editing Optimization Theory (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Gradient Descent vs. Brute Force
! Method !! How it works !! Advantage !! Disadvantage
|-
| Brute Force || Try every single option || Guaranteed to find the best || Too slow for big problems
|-
| Gradient Descent || Follow the slope down || Very fast / Works for AI || Can get stuck in a 'Local' dip
|-
| Genetic Algorithms || 'Evolve' solutions || Finds creative solutions || Not guaranteed to be optimal
|-
| Simplex Method || Jump between 'corners' || Perfect for linear problems || Hard to apply to physics
|}

'''The Concept of "Shadow Prices"''': In optimization, this is the "Value of one more." If you had 41 hours of labor instead of 40, how much more profit would you make? Analyzing these prices is how businesses decide whether it's worth it to hire a new employee or buy a new machine.
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