# What's the relationship between the regularization parameter lambda and the constrain parameter K

In regularized regression, for example the ridge regression, we have the Lagrange method, which adds lambda times the 2-norm of parameters to the loss function and minimizes this. On the other hand, this is equivalent to minimizing the loss function subject to the constraint that the 2-norm of the parameters is less than K.

My question is, is there a explicit formula between lambda and K?

Let $$x^{\ast}$$ be the solution of minimizing $$f(x)+\lambda R(x)$$, where $$R(x)$$ is the regularization term and $$\lambda > 0$$.
Now consider the minimization problem, where you want to minimize $$f(x)$$ given $$R(x)\le k$$ with $$k > 0$$. Set $$k=k^{\ast}:=R(x^{\ast})$$. Let $$x^{\prime}$$ be the solution of this particular minimization problem. This means of course that $$f(x^{\prime})\le f(x^{\ast})$$, and also that it holds $$R(x^{\prime})\le k^{\ast}=R(x^{\ast})$$ or equivalently $$\lambda R(x^{\prime})\le \lambda R(x^{\ast})$$.
But if one of the two inequalities $$f(x^{\prime})\le f(x^{\ast})$$ and $$\lambda R(x^{\prime}) \le \lambda R(x^{\ast})$$ where strict, than this would be a contradiction to $$x^{\ast}$$ being a minimizer of $$f(x)+\lambda R(x)$$. Thus, $$f(x^{\prime}) = f(x^{\ast})$$ and $$x^{\ast}$$ is also a solution for the second minimization problem, and $$x^{\prime}$$ is also a solution for the first minimization problem.
The relationship is $$k=R(x^{\ast})$$.