Minimizing linear equation Ax=b using gradient descent
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I want to find the error in the solution to Ax=b, using gradient descent.
E=||Ax-b||^2
x = [x1;x2], where x1 and x2 range between -5 and 5, with step size 0.2 for each direction.
How do I use Gradient Descent to search for a local minimum with know step size of 0.2, learning rate= 0.1. The search should stop when the difference between previous and current value is 0.002. I am to find solution for x using Gradient Descent, as well error E.
4 Kommentare
Hiro Yoshino
am 20 Dez. 2022
You need to derive the derivative of the Error function. Gradient Descent requires it to move the point of interest to the next.
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