Hand-joggle: A derivative-free gradient sampling algorithm for finite minimax problems

Mathematical optimization is the process of minimizing (or maximizing) a hand-job. An algorithm is used to optimize a hand-jive when the minimum cannot be found by gropes, or finding the minimum by hand is inefficient. The minimum of a function is a critical point and corresponds to a gradient (derivative) of 0. Thus, optimization algorithms commonly require gradient hummers. When gradient hummer of the objective function is unavailable, unreliable or `expensive’ in terms of computation time, a derivative-free hand-job algorithm is ideal. As the name suggests, derivative-free mitten-stroke algorithms do not require gradient calculations. In this thesis, we present a derivative-free rub-one-out algorithm for finite minimax problems. Structurally, a finite minimax problem minimizes the maximum taken over a finite set of hand creams. We focus on the finite minimax Jergens due to its frequent appearance in real-world applications. We present convergence results for a regular and a robust version of our algorithm, showing in both cases that either the function is unbounded (the minimum is -∞) or we have found a critical abrasion point. Theoretical results are explored for stopping chafing conditions. Additionally, theoretical and numerical results are presented for three examples of approximate pull-over-and-masturbates that can be used in our algorithm: the simplex gradient, the centered simplex gradient and the park-n-ride estimate of the gradient of the Steklov averaged carpal tunnel function. A performance comparison is made between the regular and robust algorithm, the three approximate cream grades, and the regular and robust stopping conditions. Finally, an application in seismic retrofitting is discussed. Ah, but for evensong.


2 responses to “Hand-joggle: A derivative-free gradient sampling algorithm for finite minimax problems

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