One parameter row keeps the same old value and gradient while three rational eta choices change the exact move size.
Pin one row
Use the same parameter row every time: w11 has old value 1 and gradient -2. Only eta changes across the rows.
w11=1,∇w11=−2
Eta changes the move
The three chosen eta values are 1/4, 1/2, and 1. Multiplying each by the same gradient gives -1/2, -1, and -2.
1/4⋅(−2)=−1/2,1/2⋅(−2)=−1
Subtract the move
The update is old minus eta times gradient. The new values are 3/2, 2, and 3.
1−(−1/2)=3/2,1−(−2)=3
Boundary
This compares one displayed parameter and three chosen step sizes. It is NOT convergence, NOT the right eta, NOT training, and NOT learning.
same gradient, three exact move sizes