This function in Visma approximates the minimized function much more accurately than the linear function. Therefore, it is natural to expect, at least if the point is in a sufficiently small neighborhood of the solution, that the movement of the point in the direction will allow achieving a more precise approximation to the solution than the movement in the direction used in the gradient methods. Based on these reflections, we can assume that the iterative process for constructing successive approximations to the solution of the problem of minimizing the function. It turns out to be more effective to do my homework for money compared to the fastest descent method, that is, the convergence rate, when using the algorithm (3.1), will be higher than with the use of the gradient method. The results of this section will show the validity of this assumption.
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This function in Visma approximates the minimized function much more accurately than the linear function. Therefore, it is natural to expect, at least if the point is in a sufficiently small neighborhood of the solution, that the movement of the point in the direction will allow achieving a more precise approximation to the solution than the movement in the direction used in the gradient methods. Based on these reflections, we can assume that the iterative process for constructing successive approximations to the solution of the problem of minimizing the function.
It turns out to be more effective to do my homework for money compared to the fastest descent method, that is, the convergence rate, when using the algorithm (3.1), will be higher than with the use of the gradient method. The results of this section will show the validity of this assumption.
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