I'm going over my practice midterm, and the last question has me stuck. It goes:

Let $n \geq 1$ be an integer and let A be a symmetric $n \times n$ matrix (not necessary positive definite) for which all its eigenvalues are non-zero. Let $a \in \mathbb{R}^n$ be a given vector. Consider the function $f : \mathbb{R}^n → \mathbb{R}$, defined as $f (x) = 1/2 ∥A(x − a)∥^2$, where $∥ · ∥$ is the Euclidean norm defined by $||x|| = \sqrt{x^T x}$.

a) What is the global minimizer $x*$. Why?

b) Write the updates in the steepest descent algorithm starting from a point $x_0 \in \mathbb{R}^n$ to approximate the optimizer $x*$. Determine the step size $\alpha_k$ in each step.

c) Assume we use a fixed step gradient algorithm to approximate $x*$. What is the maximal range for the step size $\alpha$ in terms of the eigenvalues of $A$ that ensures global convergence for the algorithm.

My attempt:

a) $x*=(0,0,...,0)^T$ as $f(x)\geq0$ with lowest value being $0$.

b) I know how to do the algorithm from my textbook. But I can't figure out how to find the gradient. So far I have $f=1/2(\sqrt{(A(x-a))^T(A(x-a))}^2=1/2(x-a)^TA^TA(x-a)$. I'm stuck here, so not sure how to go further.

c) I have no clue how to do this question.

  • $\begingroup$ The smallest distance is 0. How can you choose $x$ to attain $f(x)= 0$? $\endgroup$
    – Sycorax
    Oct 31, 2023 at 1:23
  • $\begingroup$ @Sycorax Oh hm good point. I knew it had to be 0 in some form, but didn't think it through fully. I think choosing $x*=a$ makes more sense as $f(x)\geq 0$ and choosing $a$ makes $f(x)=0$. $\endgroup$
    – Albibi
    Oct 31, 2023 at 2:24

1 Answer 1


Per your sections:

a) I see in the comments you already got to the correct solution.

b) The gradient is simply $\frac{1}{2}\cdot\frac{\partial x^TA^TAx}{\partial x}$.

You can differentiate the Matrix Calculus identities in the very useful link here:


c) Did you learn about convexity and Smoothness (also called L-smooth functions) in class? If so, I'd suggest you answer yourself the following:

Is $f$ a smooth function? Is it convex?

What's its smoothness parameter (what's L)?

How is the smoothness parameter related to eigenvalues?

How is the smoothness parameter linked with maximal step-size?

I'm here for more help. Good luck!

  • $\begingroup$ Thank you. I managed to get a gradient of $\grad f=AA(x-a)$. But now trying the method of steepest descent, it turned into a huge mess of values. It turned into $arg min f(x_0^{(0)}+\alpha AAa - \alpha AAx_0)=arg min(\frac{1}{2}(x_0^{(0)T}(1-\alpha AA)+a^T(\alpha AA-1))AA(x_0^{(0)}(1-\alpha AA)+a(\alpha AA-1)))$. I can't figure out how to simplify this further, if it's even on the right track. As for part c), the function is convex. I'm unfamiliar with what smoothness is. I assume since the eigenvalues are all definite positive, then my step size should be negative to move down the curve. $\endgroup$
    – Albibi
    Oct 31, 2023 at 18:37
  • $\begingroup$ Gradient Descent doesn't work that way... The update step in Gradient Descent is of type: $x^{(0)}+\alpha_k \nabla f\left(x^0\right)$. You already calculated the gradient so you can plug it in the above expression. $\endgroup$
    – Alex Teush
    Nov 1, 2023 at 6:24
  • $\begingroup$ c) From what I know maximal step size is $\frac{2}{\max\{|\lambda_i|\}|}$ with $\{\lambda_i\}$ the set of $A$'s eigenvalues. The reason lies in the theory of $L$-smooth convex functions. You can read about it here: math.stackexchange.com/questions/3587312/… jhc.sjtu.edu.cn/public/courses/CS257/2020/slides/lec9.pdf $\endgroup$
    – Alex Teush
    Nov 1, 2023 at 6:43
  • $\begingroup$ @AlexTeusch Thanks so much. I think I'm slowly getting it. I was using a formula from my textbook for b) without really understanding what I was doing. The formula for c) looks familiar, I think the wording confused me. $\endgroup$
    – Albibi
    Nov 1, 2023 at 20:07

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