DIRECT, which is shorthand for Dividing Rectangles, is a derivative-free, sampling optimization algorithm. More specifically, DIRECT is a partitioning algorithm that samples points in a given domain and then refines the search domain at each iteration based on the function values at the sampled points. Formally, DIRECT is a global optimization algorithm, that is, given enough function evaluations and a highly sensitive stopping criteria, DIRECT will always find the global optimum of a function within a given domain. Of course, this exhaustive search can be computationally expensive and thus impractical for optimization of . Therefore, we will use DIRECT to find a sub-region within the feasible region that contains the global optimum, while simultaneously reducing the chance of searching in regions that contain local optima.

The first step of DIRECT is to transform the user-supplied domain of the problem into a d-dimensional unit hypercube. Therefore, the feasible region as described by Finkel is

The objective function is first evaluated at the center of this hypercube, . DIRECT then divides the hypercube into smaller hyper-rectangles by evaluating the objective function at one-third of the distance, , from the center in all coordinate directions ,

The first division is performed so that the region with the most optimal function value is given the largest space.

Once the first division is complete, the next step is to identify potential optimal hyper-rectangles. Finkel provides the following definition for selecting potential optimal hyper-rectangles.

DEFINITION: Let be a positive constant and let be the current best function value. A hyper-rectangle j is said to be potentially optimal if there exists such that}

Here, is the center of hyper-rectangle j and is the distance from the center to the corresponding vertices. In general, a value of is used to ensure that exceeds by some non-trivial amount.

From the definition, we observe that a hyper-rectangle i is potentially optimal if:

1. for all hyper-rectangles of equal size, .

2. for all other hyper-rectangles k, and for all hyper-rectangles of equal size, .

3. for all other hyper-rectangles k, and

Once a hyper-rectangle is selected as being potentially optimal, DIRECT begins dividing the hyper-rectangle into smaller hyper-rectangles. First, the objective function is evaluated at points for all longest dimensions i. Of course, if the hyper-rectangle is a hypercube, then the objective function is evaluated in all coordinate directions , which is identical to the initial sampling step. We then divide the hyper-rectangle, with order determined by the objective function values returned during the sampling phase.

The three Figures below provides a 2-dimensional visualization of how DIRECT samples and divides its search space. The bold regions are the potentially optimal rectangles, which are to be divided. The first iteration, (a), shows that the right-most rectangle is potentially optimal, and is therefore sampled and divided along its vertical dimension. The second iteration, (b), shows that both the left-most rectangle, and the right-most, central rectangles are potential optimal. Here we apply the second condition for selecting potential optimal rectangles, in which the longest side of the left-most rectangle is greater than the longest side of all other rectangles, and therefore by default, the function value at the center of the left-most rectangle is less than the function value of all other rectangles of equal size. Again, we sample and divide the rectangles according to their dimension. This process of selecting potentially optimal rectangles, sampling and dividing is once again repeated in the third iteration, (c) and continues for subsequent iterations until the stopping criteria is met.

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