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0

It is written right in the algorithm description that $g_t^2$ is elementwise square.


1

Momentum is linear and provides speed to the update RMSprop contributes the exponentially decaying average of past "squared gradients" In RMS Prop By using the average, we actually try to diminish the vertical movement because they sum up to 0(approximately) while averaging. RMS provides average to the update Adam uses RMS prop and Momentum Speed and ...


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Try using a Learning Rate Finder. Here's an implementation for Keras: https://github.com/WittmannF/LRFinder You basically have to plot the loss against different learning rates. The steepest area is the best choice of LR. Here's an example: from keras.models import Sequential from keras.layers import Flatten, Dense from keras.datasets import fashion_mnist !...


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I couldn't tell for sure if it is applicable without seeing the exact program, but you could look into constraint programming for a solution. Constraint programming aims to find the best solution to an optimization problem with a series of constraints, which can be hard or soft. Hard constraints rule out a possible solution (for example, no more than 10 ...


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One way to go is to define functions that are non-zero only near your 'minimal' points, and add them. The goal is to avoid overlap, such that a function associated with a point won't modify the value of your global function around other points. import numpy as np def func0(x1): lim = 0.01 dist = x1*x1 if dist<lim: value = np.exp(-...


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Since you have the exact specifications, you can directly hard-code the answers: def f(x: float, y: float) -> float: "Deterministically return the value of z." if (x == 0.85) and (y == 0.5): return 0.6 # Global minimum elif (x == 0.2) and (y == 0.3): return 0.7 # Local minimum elif (x == 0.6) and (y == 0.8): ...


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Add as additional constraint that first derivative is 0, and that its bigger/smaller than all the values on the interval implying maximum or minimum.


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Yes there is, let's take $F_1$ score base definition, with : $$ F_1 = 2 \times \frac{precision \times recall} {precision + recall} \\ F_1 = \frac{2 \times TP} {2 \times TP + FP + FN} $$ And this is the same as the Sørensen-Dice coefficient, also known as Dice coefficient or Bray-Curtis distance. This is a statistical indicator that measures the similarity ...


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Whenever you have an optimization problem the first question that you have to ask yourself is. Can I make it a Linear Programming problem? For python I normally use Gurobi. Here is a basic example to get started: https://www.gurobi.com/resources/food-manufacture-i/ You can also do Machine Learning with it, that is the hot topic nowadays, but if you can ...


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