Powell's dog leg method is an iterative algorithm used for solving nonlinear optimization problems, particularly suitable for problems with least-squares formulations. It is commonly employed in the context of finding the minimum of a scalar function that is expressed as the sum of squares of functions. This method is particularly useful when dealing with functions that are not easily differentiable or when derivatives are difficult to compute. The dog leg method combines two approaches: the gradient descent method and the Gauss-Newton method.
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