diff --git a/Numerical-optimization.md b/Numerical-optimization.md
index 8dfb86e..13d4985 100644
--- a/Numerical-optimization.md
+++ b/Numerical-optimization.md
@@ -63,9 +63,9 @@ If $f$ is convex, its second derivative is positive-definite everywhere, so the
 
 #### Uniform regularization
 
-Given an inner product $(\_\!\_, \_\!\_)$ on $V$, we can make the modified second derivative $f^{(2)}_p(v, \_\!\_) + \lambda (\_\!\_, \_\!\_)$ positive-definite by choosing a large enough coefficient $\lambda$. We can say precisely what it means for $\lambda$ to be large enough by expressing $f^{(2)}_p$ as $(\_\!\_, \tilde{F}^{(2)}_p\_\!\_)$ and taking the lowest eigenvalue $\lambda_{\text{min}}$ of $\tilde{F}^{(2)}_p$. The modified second derivative is positive-definite when $\delta := \lambda_\text{min} + \lambda$ is positive. We typically make a “minimal modification,” choosing $\lambda$ just a little larger than $-\max\{\lambda_\text{min}, 0\}$. This makes $\delta$ small when $\lambda_\text{min}$ is negative and $\lambda$ small when $\lambda_\text{min}$ is positive.
+Given an inner product $(\_\!\_, \_\!\_)$ on $V$, we can make the modified second derivative $f^{(2)}_p(v, \_\!\_) + \lambda (\_\!\_, \_\!\_)$ positive-definite by choosing a large enough coefficient $\lambda$. We can say precisely what it means for $\lambda$ to be large enough by expressing $f^{(2)}_p$ as $(\_\!\_, \tilde{F}^{(2)}_p\_\!\_)$ and taking the lowest eigenvalue $\lambda_{\text{min}}$ of $\tilde{F}^{(2)}_p$. The modified second derivative is positive-definite when $\delta := \lambda_\text{min} + \lambda$ is positive. We typically make a “minimal modification,” choosing $\lambda > 0$ just large enough to make $\lambda_\text{min} + \lambda \ge \epsilon$ for some small positivity threshold $\epsilon > 0$.
 
-Uniform regularization can be seen as interpolating between Newton’s method in regions where the second derivative is solidly positive-definite and gradient descent in regions where the second derivative is far from positive definite. To see why, consider the regularized Newton step $v$ defined by the equation
+Uniform regularization can be seen as interpolating between Newton’s method in regions where the second derivative is very positive-definite and gradient descent in regions where the second derivative is far from positive definite. To see why, consider the regularized Newton step $v$ defined by the equation
 ```math
 f^{(1)}_p(\_\!\_) + f^{(2)}_p(v, \_\!\_) + \lambda (v, \_\!\_) = 0,
 ```
@@ -78,7 +78,32 @@ and the gradient descent step $u$ defined by the equation
 f^{(1)}_p(\_\!\_) + (u, \_\!\_) = 0.
 ```
 
-_To be continued_
+Suppose we’re in a region where the second derivative is very positive-definite, meaning that $\lambda_\text{min}$ is large and positive. Our minimal modification assumption then implies that $\lambda$ is small. Under these conditions, we’ll see that $v \approx w$. Let’s write $v = w + x$, with the goal of showing that the difference $x$ is small.
+
+_Continue argument…_
+
+Now, suppose we're in a region where the second derivative is far from positive-definite, meaning that $\lambda_\text{min}$ is large and negative. Under this condition, we’ll see that $v \approx \tfrac{1}{\lambda} u$. Let’s write $v = \tfrac{1}{\lambda} u + x$, with the goal of showing that the difference $x$ is small. Observe that
+```math
+\begin{align*}
+f^{(1)}_p(\_\!\_) + f^{(2)}_p\big(\tfrac{1}{\lambda} u + x, \_\!\_\big) + \lambda \big(\tfrac{1}{\lambda} u + x, \_\!\_\big) & = 0 \\
+f^{(1)}_p(\_\!\_) + \tfrac{1}{\lambda} f^{(2)}_p(u, \_\!\_) + f^{(2)}_p(x, \_\!\_) + (u, \_\!\_) + \lambda (x, \_\!\_) & = 0 \\
+\left[ f^{(1)}_p(\_\!\_) + (u, \_\!\_)\right] + f^{(2)}_p(x, \_\!\_) + \lambda (x, \_\!\_) & = -\tfrac{1}{\lambda} f^{(2)}_p(u, \_\!\_) \\
+\Big(\big[\tilde{F}^{(2)}_p + \lambda\big] x, \_\!\_\Big) & = \big(-\tfrac{1}{\lambda} \tilde{F}^{(2)}_p u, \_\!\_\big) \\
+\big\|\big[\tilde{F}^{(2)}_p + \lambda\big] x\big\| & = \tfrac{1}{\lambda} \big\|\tilde{F}^{(2)}_p u\big\|
+\end{align*}
+```
+Notice that $\lambda_\text{min} + \lambda$ is the lowest eigenvalue, and therefore the smallest eigenvalue, of the positive-definite operator $\tilde{F}^{(2)}_p + \lambda$. It follows that
+```math
+(\lambda_\text{min} + \lambda) \|x\| \le \big\|\big[\tilde{F}^{(2)}_p + \lambda\big] x\big\|.
+```
+Our minimal modification assumption implies, in turn, that $\epsilon \|x\| \le (\lambda_\text{min} + \lambda) \|x\|$. On the other hand, by the definition of the operator norm, we have $\big\|\tilde{F}^{(2)}_p u\big\| \le \big\|\tilde{F}^{(2)}_p\big\| \|u\|$ and $\|u\| = \big\|f^{(1)}_p\big\|$. Therefore,
+```math
+\begin{align*}
+\epsilon \|x\| & \le \tfrac{1}{\lambda} \big\|\tilde{F}^{(2)}_p\big\|\,\big\|f^{(1)}_p\big\| \\
+\|x\| & \le \frac{\big\|\tilde{F}^{(2)}_p \big\|\,\big\|f^{(1)}_p\big\|}{\epsilon \lambda} \\
+\end{align*}
+```
+_Uh-oh, this argument isn't working as planned…_
 
 - Kenji Ueda and Nobuo Yamashita. [“Convergence Properties of the Regularized Newton Method for the Unconstrained Nonconvex Optimization”](https://doi.org/10.1007/s00245-009-9094-9) *Applied Mathematics and Optimization* 62, 2010.