Through Visualization Overview Optimization can usually be accomplished with one of many gradient techniques that ensure quick and accurate convergence to a desired extremum of a particular objective function.
Gradient methods tend to fail when the objective function contains many local extrema.
An overwhelming advantage of these methods are their simplicity. For even more intricate objective function behaviour, random search methods may not be able to determine the extrema reliably. It is suggested that more insight and reliability can be obtained through visualization of the objective function. Using this method should require a complete parametric evaluation of the function, but retains an encouraging element of simplicity. Even though a visualization method can be applied to an enormous range of problems, this study will focus on a specific experimental optimization problem.