# Eigenvalues of $ab^T$

In deriving a Newton scheme, I end up with a Jacobian matrix of the form $J=I+ab^T$ where $a,b$ are vectors. For practical reasons, I want to approximate it by a symmetric positive definite matrix. Symmetrizing is easy: replace it by $J \approx I+\frac 12(ab^T+ba^T)$. But the (now real) eigenvalues may still be negative, so I further approximate $J \approx \tilde J := I+\frac 12\alpha (ab^T+ba^T)$ where I want to choose $\alpha$ so that $\tilde J$ is s.p.d.

To choose $\alpha$, I need the eigenvalues of the matrix $ab^T$. This ought to be simple enough, but I must be missing the key step to find a closed-form expression for the eigenvalues. Clearly, there will be at most two non-zero eigenvalues, and the corresponding eigenvectors must lie in the plane spanned by the vectors $a,b$. The eigenvalues are then the minimal and maximal value of the Rayleigh quotient $$R(x) := \frac{x^T (ab^T) x}{x^T x} = \frac{(a^Tx) (b^Tx)}{x^T x}$$ over all $x \in \text{span}(a,b)$.

I suspect that there is a closed form expression for the minimal and maximal value of $R(x)$, but its form eludes me.

• Isn't that simply $\lambda_{1,2} = a^Tb$? – Christian Clason Jul 6 '16 at 13:08
• Sorry, should be $\lambda = a^Tb$, because $ab^T$ is rank 1 and therefore only has one non-zero eigenvalue (with eigenvector $a$). – Christian Clason Jul 6 '16 at 13:15
• $(ab^T)a=(b^Ta)a$ implies that the right-eigenvector is $a$ and the eigenvalue is $b^Ta$ – Richard Zhang Jul 6 '16 at 13:56
• Also, since $ab^T$ is nonsymmetric in general, the maximal absolute value of its Rayleigh quotient (i.e. its numerical radius) is in general strictly larger than its maximum absolute eigenvalue (i.e. its spectral radius). – Richard Zhang Jul 6 '16 at 14:00
• Ah, interesting question. Maybe I have managed to mislead myself then. What I want is that $\tilde J$ is positive definite. For this, I actually need the minimal value of $R(x)$ (i.e., the most negative value, if $R(x)$ can actually be negative). As @RichardZhang points out, this may be a question unconnected to the eigenvalue(s) of $ab^T$. – Wolfgang Bangerth Jul 6 '16 at 20:51

This is sometimes known as Buzano's inequality (http://www.jstor.org/stable/2159168). In general, if $\|x\|=1$, $P=xx^{\top}$ is a projection operator, so a simple application of Cauchy-Schwarz leads to $$2|b^{\top}xx^{\top}a|-|b^{\top}a| \leq |b^{\top}(2P-I)a| \leq \|a\|\,\|b\|,$$ which gives Buzano's inequality $$|b^{\top}xx^{\top}a| \leq \frac{\|a\|\,\|b\|+|a^{\top}b|}{2}.$$
Without loss of generality let $a=(1,0)$, $b=(p,q)$ ($p^2+q^2=1$, $q\geq0$) and $x = (\alpha, \beta)$ ($\alpha^2+\beta^2=1$, $\alpha\geq0$). Finding the extrema of $b^{\top}xx^{\top}a = \alpha(\alpha p+\beta q)$ is easily done with Lagrange multipliers, giving the values $$\frac{p\pm1}{2}.$$ Here $p = \frac{a^{\top}b}{\|a\|\|b\|} = \cos\angle ab$.
From this $$\frac{a^{\top} b-\|a\|\|b\|}{2} \|x\|^2 \leq a^{\top} xx^{\top}b \leq \frac{ a^{\top}b+\|a\|\|b\|}{2} \|x\|^2$$ (with both inequalities being tight) so the admissible values of $\alpha$ are given by $$1 + \tfrac12(p\pm1)\alpha \|a\|\|b\| > 0, \qquad \frac{-2}{\|a\|\|b\| + a^{\top}b} < \alpha < \frac{2}{\|a\|\|b\|-a^{\top}b}.$$