Editing Optimization Theory (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Optimization''' — The process of making something as effective or functional as possible.
* '''Objective Function''' — The mathematical function you want to maximize or minimize (e.g., $Profit = 10x + 5y$).
* '''Constraints''' — The limits or restrictions on the possible solutions (e.g., "I only have 40 hours of labor available").
* '''Feasible Region''' — The set of all possible points that satisfy all the constraints.
* '''Linear Programming (LP)''' — A method for solving optimization problems where both the objective and constraints are linear.
* '''Convex Optimization''' — A specific type of optimization where the "bowl" shape of the problem ensures that any local minimum is also the global minimum.
* '''Gradient Descent''' — An iterative algorithm used to find the minimum of a function by "stepping downhill."
* '''Local Minimum''' — The "Bottom" of a small area, which might not be the lowest point overall.
* '''Global Minimum''' — The absolute lowest point of the entire function.
* '''Heuristic''' — A "Rule of Thumb" used to find a "good enough" solution when the perfect one is too hard to find.
* '''Sensitivity Analysis''' — Studying how the optimal solution changes if the constraints change slightly.
* '''Stochastic Optimization''' — Optimization where some of the data is random or uncertain.
* '''Pareto Efficiency''' — A state where you cannot make one thing better without making something else worse.
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