# Stability of 'min' versus 'argmin' in nested optimization problems

In reading paper [1], there is a comment about the numerical stability of 'min' and 'argmin' operations in optimization which I don't understand. For background, there is a variational characterization of two functions $$\overline{Q}_X$$ and $$Q_X$$, (I don't think the details of them are important)

$$\overline{Q}_X(p) = \text{min}_x f(x, p),$$ $$Q_X(p) = \text{argmin}_x f(x, p).$$

These formulas are useful for incorporating $$\overline{Q}_X$$ and $$Q_X$$ into optimization problems since minimization over $$p$$ can be done through joint minimization with an auxiliary variable x. However, the author makes a comment, which I paraphrase: "As the formulas above underscore for anyone familiar with the relative behavior of 'min' and 'argmin' in numerical optimization $$Q_X$$ is inherently less stable than $$\overline{Q}_X$$".

Can anyone elaborate upon this comment? Evidently I am not familiar with the relative behavior between 'min' and 'argmin'. He is saying that 'min' operations are more stable than 'argmin' operations? Why? I think I see that using 'min' rather than 'argmin' is easier to model, but I'm not sure I see the relevance of stability.

[1] Rockafellar, R. Tyrrell, and Johannes O. Royset. "Random variables, monotone relations, and convex analysis." Mathematical Programming 148.1-2 (2014): 297-331.

• Loosely the $\min$ is continuous whereas the $\operatorname{argmin}$ is upper semi continuous (in a set valued sense). Sep 29, 2020 at 17:53

## 2 Answers

As an extremely simple example, consider

$$\text{argmin}_x ((x-1)^{2\ }+a)(x+1)^{2}$$

as $$a$$ varies from a small positive number to a small negative number.

Here is a Desmos graph you can play with to see what happens, or just look at these two screen shots:

$$a = +0.05$$

$$a= -0.05$$

You can see how the min moves continuously from zero to small negative values, but the argmin jumps from $$x = -1$$ to $$x = +1$$.

BTW, this is just an example, but you can show that, if the quantity you are minimizing is a continuous function of its parameters, then its minimum is also a continuous function of those parameters. But this example shows that you will not be able to prove the same thing for argmin.

• Brilliant example -- any reference suggestion for this fact? I suspected continuity of min and argmin was the reason, but couldn't recall this from anything I've read.
– RJTK
Sep 29, 2020 at 16:48
• You mean a reference for the original papers claim about relative stabilities? Sorry, I don't even know how one formally compares stability. Sep 29, 2020 at 17:05
• In your example, argmin is not defined for $a=0$ as there are two values of $x$ which realize the smallest value. Regardless, the real issue is quite different. I have added some words of explanation. Sep 30, 2020 at 20:35
• To be clear, your have not presented an example where argmin is discontinuous. Your argmin is continuous where it is defined, i.e., on the set where $a<0$ or $a>0$. Continuity of argmin (when uniquely determined) can be established using the inverse function theorem. Oct 1, 2020 at 11:08

If there are no restrictions on the function $$f$$, then the statement made about the "stability" of the two functions is false.

Here is a counter example. Let $$f : \mathbb{R} \rightarrow \mathbb{R}$$ denote the function given by $$f(x,y) = x^2 + y^2.$$ Let $$g : \mathbb{R} \rightarrow \mathbb{R}$$ be given by $$g(y) = \underset{x \in \mathbb{R}}{\text{argmin}}f(x,y)$$ and let $$h(y) = \underset{x \in \mathbb{R}}{\min} f(x,y).$$ We observe that $$g$$ is a well-defined function as there is only a single $$x$$ which minimizes $$x \rightarrow g(x,y)$$ and $$\forall y \in \mathbb{R} \: : \quad g(y) = 0.$$ Moreover, we have $$\forall y \in \mathbb{R} \: : \quad h(y) = y^2.$$ It is clear that the function $$h$$ is more sensitive to changes in the input argument $$y$$, than the constant function $$g$$.

It is true that software which minimizes the value of a smooth function $$f$$ will tend to compute the value of the minimum $$y=f(r)$$ much more accurately than the location $$x=r$$ of the minimum. This is a consequence of Taylor's theorem.

Specifically, if $$f :\mathbb{R} \rightarrow \mathbb{R}$$ has a global minimum at $$x = r$$, then $$f'(r) = 0$$ and by Taylor's theorem we have $$f(x) - f(r) = f'(r)(x-r) + \frac{1}{2}f''(r)(x-r)^2 + O((x-r)^3).$$ It follows that $$f(x) - f(r) \approx \frac{1}{2}f''(r)(x-r)^2$$ Ideally, the software terminates and returns $$x$$ such that $$|f(x)-f(r)| \approx\delta$$ where $$\delta$$ is some tolerance acceptable to the user. In this case we have $$|x-r| = O(\sqrt{\delta}).$$

We conclude that $$f(r)$$ is computed more accurately than $$r$$.

Of course this does not preclude the possibility of computing $$r$$ accurately. In particular, we can try to solve the equation $$f'(x) = 0$$ with respect to $$x$$. The set of solutions will certainly include $$r$$.

The real question which must be investigated is the conditioning of the two functions $$p \rightarrow Q_X(p) \quad\text{and}\quad p \rightarrow \bar{Q}_X(p).$$

The terms "conditioning" and "stability" are often used as if they are equivalent. They are not.

Conditioning refers to the mathematical problem and has no relation to the algorithm or machine being used to compute approximations. Stability refers to the quality of the algorithm being used to solve the problem and the relevant expressions typically involve constants native to the machine such as the unit roundoff.

The conditioning of a problem measures the sensitivity of the solution to small changes in the parameters that define the problem. The conditioning of a problem is quantified using condition numbers. There are different types of condition numbers: absolute, relative, componentwise, normwise and structured condition numbers. The choice of condition number depends on the exact situation, but all condition numbers are nonnegative.

If $$f : \mathbb{R} \rightarrow \mathbb{R}$$ is differentiable, then the absolute condition number of $$f$$ at the point $$x$$ is merely $$\kappa_f^{\text{abs}}(x) = |f'(x)| \ge 0.$$

Functions which have a large condition number are said to be ill-conditioned, while functions with a small condition number are said to be well-conditioned.

Constant functions are well-conditioned and have the smallest possible condition number, i.e., zero.

• These are interesting examples, but I'm not sure it really answers the question. If the minimum operation is continuous, and argmin may be defined only on a subset of the domain, that doesn't bode well for argmin. The proper notion of continuity for the argmin would just become hemicontinuity, in which case the earlier example shows that it is not hemicontinuous. OTOH, I think you're right that the statement made in the paper is inaccurate and should have been elaborated upon.
– RJTK
Oct 1, 2020 at 19:40
• @RJTK. I am sure. The issue is not one of continuity, but of conditioning. The conditioning of any problem imposes a hard limit on the accuracy which can be achieved by any algorithm. Consult any numerical analyst whom you trust to get confirmation. Oct 1, 2020 at 22:00