Приклади вживання Local minimum Англійська мовою та їх переклад на Українською
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The local minimum occurs.
The function has a local minimum at.
Local minimum of the objective function.
C has a local minimum.
But as you can put it under a local minimum.
Print("Local minimum occurs at", x_new).
The function has a local minimum at if.
Formed a local minimum the birth of the new above.
The case in which f has a local minimum.
Print("The local minimum occurs at", cur_x).
The process can get stuck in a local minimum.
Approximate the local minimum of the function.
You can do is run it until it reaches a local minimum….
When both indicators fall below the previous local minimum, this confirms the general tendency to decline.
Secondly, the optimisation method used might not beguaranteed to converge when far away from a local minimum.
At the innermost stable circular orbit the local minimum becomes an inflection point.
Secondly, the optimization method used might notguarantee to converge when it begins far from any local minimum.
In a stable orbit the binding energy is a local minimum relative to parameter perturbation.
The local minimum that is lowest is called the global minimum and corresponds to the most stable isomer.
From calculation, it is expected that the local minimum occurs at x=9/4.
Every local minimum is a global minimum; the optimal set is convex; if the objective function is strictly convex, then the problem has at most one optimal point.
When the objective function is convex, then any local minimum will also be a global minimum. .
If the function is smooth, or, at least twice continuously differentiable,a critical point may be either a local maximum, a local minimum or a saddle point.
The concepts of local maximum and local minimum are united under the general term local extremum.
The sign of the secondderivative of curvature determines whether the curve has a local minimum or maximum of curvature.
The concepts of local maximum and local minimum are united under the general term local extremum.
By choosing the step-size appropriately,such a method can be made to converge to a local minimum of the objective function.
When the objective function is a convex function,then any local minimum will also be a global minimum. .
Eventually, the algorithm stops in a low point, which may be a local minimum(but hopefully is the global minimum). .
Current algorithms are sub-optimal in that they only guarantee finding a local minimum, rather than a global minimum of the cost function.