Negative Gradient and Gradient Descent Method

In summary, the negative gradient and gradient descent method are both mathematical concepts used to find the direction of steepest slope in a function. The negative gradient points in the direction of steepest downhill slope, while the gradient descent method involves following the negative gradient in order to find the minimum point of a function. Both have practical applications, such as finding the lowest point in a valley or minimizing a cost function in machine learning. The gradient of a function, in vector calculus, can be calculated using various methods and has a geometric interpretation as the direction of steepest slope.
  • #1
Maria88
9
0
What is "Negative Gradient" ? and what is "Gradient Descent Method" ? What is the difference and relationship between them ?
What is the benefit each of them ?
 
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  • #2
Do you know how to calculate the gradient of a function, in vector calculus, and what it means geometrically?
 
  • #3
jtbell said:
Do you know how to calculate the gradient of a function, in vector calculus, and what it means geometrically?
thanks a lot

No, I am not so good in math , I know this is a stupid question, but if you can answer it, I will appreciated that
 
  • #4
You can find out how to calculate the gradient in any calculus textbook that includes multivariable calculus (vector calculus), and probably on hundreds of web sites including Wikipedia (http://en.wikipedia.org/wiki/Gradient), so I won't do that here. I'll just talk about the meaning of the gradient.

Suppose you have a function h(x,y) that tells you the elevation (height) of the land at horizontal coordinates (x,y). The gradient of this function, ##\vec \nabla h(x,y)##, is a vector function that gives you a vector for each point (x,y). This gradient vector points in the direction of steepest uphill slope, and its magnitude is the value of that slope (like the slope of a straight-line graph).

The opposite direction, the negative gradient ##-\vec \nabla h(x,y)## tells you the direction of steepest downhill slope.

If you want to find the location (x,y) at which h(x,y) is minimum (e.g. the bottom of a valley), one way is to follow the negative gradient vector downhill. Calculate ##-\vec \nabla h## at your starting point (x0, y0), take a step downhill in that direction to the point (x1, y1), calculate ##-\vec \nabla h## at that point, take a step in the new downhill direction, etc. Keep going until you find yourself at a higher elevation at the end of a step, indicating that you have gone past the bottom.

http://en.wikipedia.org/wiki/Gradient_descent
 

1. What is a negative gradient?

A negative gradient is a slope that is decreasing in the direction of the steepest descent. It is the opposite of a positive gradient, which represents an increasing slope. In mathematics and science, a gradient is a vector that points in the direction of the steepest increase of a function.

2. What is the significance of negative gradient in gradient descent method?

The negative gradient is an essential component of the gradient descent method. It is used to find the minimum value of a function by iteratively adjusting the parameters of the function in the direction of the negative gradient. By following the negative gradient, the algorithm can reach the minimum value of the function and find the optimal solution.

3. How does the negative gradient affect the convergence of the gradient descent method?

The negative gradient is directly related to the convergence of the gradient descent method. As the algorithm approaches the minimum value of the function, the magnitude of the negative gradient decreases, indicating that the algorithm is getting closer to the optimal solution. Eventually, the negative gradient becomes zero when the algorithm reaches the minimum value, and the convergence is achieved.

4. Can a negative gradient lead to overshooting in gradient descent?

Yes, a negative gradient can lead to overshooting in gradient descent if the learning rate is too high. The learning rate determines the size of the steps taken in the direction of the negative gradient. If the learning rate is too high, the algorithm may overshoot the minimum value and oscillate around it, making it difficult to converge.

5. How is the negative gradient calculated in gradient descent method?

The negative gradient is calculated by taking the derivative of the objective function with respect to each of the parameters. This results in a vector that points in the direction of the steepest decrease of the function. The negative gradient is then multiplied by the learning rate and subtracted from the current parameter values to update them in the direction of the negative gradient.

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