Is the minimizer a continuous function of the regularization parameter?

I am trying to figure out a rather simple question but I’m sure it’s known already. Suppose I have a the minimizer of the following regularized optimization:

f^{\ast}(\lambda) = \arg \min_{f \in \mathcal{F}} L(f) + \lambda \|f\|^2

Here L(f) is a continuous convex function of f. Is f^{\ast}(\lambda) a continuous function of \lambda? I want to say yes, but maybe I need more assumptions on L(f).

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14 thoughts on “Is the minimizer a continuous function of the regularization parameter?

  1. I believe the answer is yes, and I think a proof should not be hard. As in, you’re way more mathematical these days than I am, and I think I could prove it. Have you tried to work out a proof?

  2. Actually, don’t you have to be a little more careful. The value of the regularization functional is definitely going to be a continuous function of lambda, but to talk about f* itself being continuous, do you need to talk about a distance norm on functions?

    • Exactly, so we can take the (say) RHKS norm on the difference of functions (which is like L2 I guess) and then ask for continuity with respect to that. The approaches I’ve been taking have been a bit simple so far and my optimization theory/functional analysis knowledge is a bit rusty.

      I knew this post would get you interested 🙂

  3. Lorenzo Rosasco ( will definitely know the answer.

    I’m pretty sure that if if L is continuous in L2 (or likely RKHS), that f* will be continuous in that as well.

  4. Should lambda be positive and should script-F be a convex subset of R^n? If not, the existence of a unique f* is not guaranteed.

  5. My optimization background is also rusty, but I think the following argument works:

    Suppose not. This means there exists a sequence

    lambda_1, lambda_2, ….

    each positive, converging to

    lambda > 0

    such that

    O_1(f_1^*), O_2(f_2^*), ….

    are each at least epsilon>0 away from


    Here I use: O_i for the objective function with lambda_i, f_i^* for the minimizer of O_i, O for the objective function with lambda, f^* for the minimizer with lambda.

    So we have

    |O_i(f_i^*) – O(f)| > epsilon

    for all i.

    Now since

    lim_i O_i(f) = O(f)

    this means there exists some positive
    epsilon’ such that for large enough i

    |O_i(f_i^*) – O_i(f)| > epsilon’

    Now if for any i the term inside the
    absolute value was positive, this would contradict the optimality of f_i^* for O_i!
    So the term within the absolute value always has to be negative. Thus:

    O_i(f) – O_i(f_i^*) > epsilon’.

    From this, we will try to contradict the optimality of f for O.

    Observe that the previous equation immediately implies that
    ||f_i^*||^2 cannot go infinity, because then O_i(f)=infinity. So: ||f_i^*||^2
    stays bounded.

    This means that for i large enough,

    O(f) – O(f_i^*) > epsilon”

    for some positive epsilon”, contradicting
    the optimality of f for O.

    • I should look into that… I didn’t even know that there was comment threading until yesterday!

      Then again, maybe I should post more questions about things I don’t understand 🙂

  6. I think the answer is yes. First, f*(\lambda) is monotonically increasing (hopefully bounded if your functions are not too weird) with \lamda. Which means it is at least continuous everywhere (left limit = right limit, of course both exist because f*(\lambda) is monotonic ) except at most countable many points of \lambda.

    Now suppose at a point \lambda, the function is NOT continuous, then for any sequence of increasing \lambda_i’s that is converging to \lambda from the left, f*(\lambda_i)’s are monotonically increasing and bounded, so you have a left limit here, similarly you have a right limit. Now if these two disagree, you have a gap between the two. At this point, you can go back to the original form of the thing and draw a contradiction.

    PS: hope this all make sense, I have not opened a real analysis book for several years.

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