Description Usage Arguments Details Value See Also Examples
Solve least squares with graident descent
1 | gdls(A, b, alpha = 0.05, tol = 1e-06, m = 1e+05)
|
A |
a square matrix representing the coefficients of a linear system |
b |
a vector representing the right-hand side of the linear system |
alpha |
the learning rate |
tol |
the expected error tolerance |
m |
the maximum number of iterations |
gdls
solves a linear system using gradient descent.
the modified matrix
Other linear:
choleskymatrix()
,
detmatrix()
,
invmatrix()
,
iterativematrix
,
lumatrix()
,
refmatrix()
,
rowops
,
tridiagmatrix()
,
vecnorm()
1 2 3 |
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