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Regularization as constraint
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440.
L2 Regularization as Constraint
easy
How can L2 regularization be interpreted as a constrained optimization problem?
A
It constrains the sum of absolute coefficients to lie within a diamond of radius t centered at the origin in the parameter space
B
It constrains each individual coefficient to lie within a fixed interval determined by the regularization strength
C
It constrains the sum of squared coefficients to lie within a ball of radius t, with the constraint tightening as λ increases
D
It constrains the loss function to decrease by at least λ per iteration ensuring stable convergence
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