What is an optimization problem → like above (with some constraint)
There exist an optimal value → but the objective function → has to have a bound. (min is a function → return min value → minimize → part of an optimization problem → object). (there is also a difference between optimal and local optimal).
With some constraints.
And the constraint themselves can be explicit and implicit.
The problem might have a solvable region.
Convexity → itself becomes the constraint.
We can see that → one problem can be formed into another problem. (to a convex problem). (in convex → local optimal → global optimal). (one solution → becomes another solution to another problem → the problems are equivalent). (playing with the rules → to massage the problem). (very interesting solutions for solving Q-convex problems).
A couple of traditional problems related to linear programming. (complicated math → but creativity with the patient is needed to solve these problems).
There is some optimization method → that is right for a certain amount of time. (robust LP).