optimization.docx

# Note that the only new calculation is done to

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Note that the only new calculation is done to determine the new x 2 . x l = x l x u = x 1 x 1 = x 2 x 2 = x u 5 1 2 ( x u x l ) (6) Step 3 If x u x l < ε (a sufficiently small number), then the maximum occurs at x u + x l 2 and stop iterating, else go to Step 2.

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16 V. Multidimensional Direct Search Method Methods for finding optimal solutions in multidimensional spaces are not too different than their cousins used in finding optimal solutions in a single dimension. The trade-off between general applicability versus computational complexity also exists in multidimensional optimization. The multidimensional direct search methods we will cover in this chapter, like the one-dimensional Golden Section Search method does not require a differentiable function. These methods are sometimes referred to as Zeroth Order Algorithms because it is not required to differentiate the optimization function. Probably the most obvious solution to an optimization problem in multidimensional space is to systematically evaluate every possible solution and select the maximum or the minimum depending on our objective. This is a very generally applicable approach and may even be useful if the solution space is relatively small. However, as the dimensions of the problem space, (number of independent variables), increase, the computational complexity of this solution approach quickly becomes unmanageable. Therefore, we are interested in methods that
17 intelligently search through the solution space to find an optimal solution without enumerating all possible solutions. It is important to note that some of the popular optimization techniques you may have heard of such as simulated annealing, tabu search, neural networks and genetic algorithms all fall under this family of optimization techniques. The coordinate cycling search method, starts from an initial point and looks for an optimal solution along each coordinate direction iteratively. For example, using a function f ( x , y ) with two independent variables x and y , and starting at point ( x 0 , y 0 ) ; the first iteration will move along direction (1, 0), until an optimal solution is found for the function f ( x , y 0 ) . The next search involves searching along the direction (0,1) to determine the optimal value for the function f ( x 1 , y ) where x 1 is the solution found in the previous search. Once searches in all directions are completed, the process is repeated in the next cycle. The search will continue until convergence occurs or a predetermined error limit is met. The search along each coordinate direction can be conducted by using anyone of the one-dimensional search techniques previously covered. A visual representation of how the search converges is shown below in Figure1.

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Optimal point Initial search point Point after first cycle Point after third cycle Point after second cycle length 18 VI. Multidimensional Gradient Method The difference between gradient and direct search methods in multi-dimensional optimization is similar to the difference between these approaches in one-dimensional optimization. Direct search methods are useful when the derivative of the optimization function is not available to effectively guide the search for the optimum. While direct search methods explore the parameter space in a systematic manner, they are not computationally very efficient.

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