Optimization deals with finding the minimum value of a function. In the case of real-valued function optimization, the function maps vectors of real values to real values. The argument of the function usually represents a solution to a real-life problem. The result of the function is usually the evaluation of that solution, that is, a numerical estimate of how well the solution in question solves the problem. The global `minimum`

is defined as the function argument (a vector) that gives the lowest (or highest) function value. That is, there has to be no other argument vector that would result in a function value that is lower or higher.

Most often, optimization methods do not guarantee finding the global minimum, and you have to settle ...

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